Education is one of the most important distinguishing features of human life. From an early age we acquire knowledge and skills, and increasingly often we go on to complete bachelor’s, engineering and master’s degrees. There are also people for whom this stage is not enough and who consider a doctorate and further personal development, seeing academic degrees and titles also as a means of distributing prestige and power [1]. One option here is study at doctoral schools affiliated with universities, chosen by young people after graduation instead of taking up paid work in business or public administration. But even those who do take up employment consider further developing their competences — not only through courses and training, but also by earning further academic degrees.

On the other hand, companies moving towards innovation and the development of their own research, development and implementation activities are increasingly looking for people capable of taking on such tasks. At a time when cooperation between academia and the socio-economic environment is becoming ever more important, the question arises of how researchers from the latter sphere can obtain academic degrees and titles on the basis of the results of their work. The doctoral degree in particular is an example of how cooperation between the academic community and the socio-economic environment can be put into practice [2], and it is recognised as such by many legal systems around the world. It is also undoubtedly the academic degree most widely used outside a purely academic setting.

It is also worth bearing in mind that the greatest intellectual potential for scientific discovery coincides with the age of the young staff in institutions, so doctoral projects hit precisely this “golden age”. Doctoral research is always an important stimulus for the development of science, while doctoral candidates from industry additionally contribute to the transfer of knowledge and skills — most often in both directions. Looking ahead to combining careers and employing doctors from industry part-time at universities, such trends and circumstances clearly have significant consequences for doctoral careers outside academia.

The past years have shown that the direction chosen was a very good one. Already after the first defences and an analysis of completed projects, it can be said that innovative solutions have not only actually been created but have also been put into practice through implementations. Today more than 3,000 people representing a wide range of disciplines and fields of science have studied or are studying within the programme. An academic career path has emerged, based on conducting research aimed at application in business practice (not only in a specific company, but often much more widely). Implementation doctoral candidates are trained not only at the university but also in companies or institutions. This new career path makes it possible to obtain a doctoral degree based also on achievements in implementation work, while mastering the researcher’s craft.

The Implementation Doctorate Programme therefore effectively helps to provide Polish companies and public institutions with highly qualified employees whose task is to use the achievements of science to create new technologies and find practical applications for them. Already now, on the basis of the first completed doctorates, it can be said that the programme has contributed to many innovative technological solutions that have been put into practice as implementations. The subject of the Ministry of Science and Higher Education programme entitled

“Implementation Doctorate” is thus to create conditions for developing cooperation between higher education and science institutions and the socio-economic environment, carried out within doctoral schools and consisting in training doctoral candidates in cooperation with the companies or other entities that employ them, resulting in the implementation in those entities of the results of the doctoral candidates’ research.

To evaluate the documents and the work carried out in projects under this programme, an advisory team to the Minister of Science and Higher Education for evaluating applications and reports under the Implementation Doctorate Programme was established. It consists of about 100 scientists with extensive experience in conducting and reviewing doctoral proceedings. Its members evaluate incoming applications on an ongoing basis (the submission deadline is always set in late spring), as well as annual and final reports. These documents are discussed at remote meetings where joint decisions are reached. The team works very intensively, dealing not only with evaluation but also devoting much of its attention to preparing application and report templates that support and simplify applying and reporting for both the doctoral candidate and the doctoral school to which they belong. The documents are modified on the basis of information obtained during project implementation.

Moreover, when discussing individual projects, team members exchange experiences on strengths and problems in carrying out the work and on ways of dealing with difficulties. These discussions gave rise to the idea for this project, as they showed that it is absolutely essential not only to monitor the programme but also to take broader action to promote it further. It was also necessary to create a database of the companies, disciplines and topics covered by implementation doctorates, so that this knowledge would not be lost but could contribute to the further development of innovative activity in industry, the business environment and the institutions that support them.

Bearing in mind that many companies — and not only small or medium-sized ones — still approach the programme’s requirements with some reserve, it is important to keep preparing explanatory materials and holding meetings that present, on the one hand, the opportunities and benefits and, on the other, the workload and commitment involved. This is one of the aims of this publication, an outcome of the project Disseminating Best Practices in the Implementation of the Industrial Doctorate Program. The publication contains research findings, analyses, model solutions, surveys and interviews, as well as a story written in a storytelling convention, as ready-made materials for further promotion of the programme. Selected elements have been published in scientific and popular-science journals as national and international promotion of implementation doctorates.

There are many types of doctoral degrees and models of doctoral education and training around the world. They are distinguished by specific features and procedures for obtaining them [4], partly with specific legal interpretations [5]. A doctorate carried out jointly with a company is called an industrial doctorate; on the one hand it emphasises the researcher’s non-academic activity, and on the other it reflects the need for, and expectation of, a degree that better meets the needs of business and the socio-economic environment [6]. The subject itself, however, is treated rather narrowly, focusing on the completion of specific pieces of work. Many countries also have their own ideas and procedures for doctorates prepared jointly with industry. The literature also includes works focusing on comparisons between countries [7].

Programmes enabling working professionals, often employed in commercial companies, to complete a doctorate operate successfully in various fields in many countries, both in Europe (including Sweden [8], Norway [9], Iceland [10], Germany [11], the United Kingdom [12, 13], Ireland [14], Portugal [15] and Italy [16]) and outside Europe (the United States [17], China [18], Australia [19, 20], South Africa [21] and Iran [22]). A number of publications have been devoted to them, although as a rule they discuss rather narrowly the cooperation between universities and industrial partners on individual doctoral theses, their quality compared with traditional doctorates, and the benefits for business partners. On this basis it can be said that industrial doctorate programmes are specialised models of doctoral education aimed at connecting academia with industry.

These programmes are being implemented ever more widely around the world, especially in highly developed countries, and are regarded as strategic tools that support innovation, improve employability and foster knowledge transfer. Around the world, doctoral candidates conduct research while being employed in companies, often working on projects initiated by those companies [23]. Joint supervision and the joint setting of project goals by universities and industrial partners are also common [24]. The emphasis is on applied research and solving real-world problems [25, 26]. Projects usually last 4–5 years, like traditional doctoral studies [27], and may also include teaching, especially in newer programmes (e.g. in Portugal) [28]. The costs and responsibility for supervision are shared between industry, academia and sometimes government institutions.

The governments of many countries actively fund and promote such programmes in order to improve doctoral candidates’ employability outside academia and to support innovation in industry. Industrial doctorate programmes generate specific benefits. These include supporting the transfer of knowledge and innovation between academia and industry, increasing the chances of doctoral graduates finding employment in non-academic sectors [29] and developing cooperation networks [30]. But there are also certain challenges: the need to operate in different organisational cultures and reconcile different expectations, managing distributed supervision and responsibility for the project [31], and ensuring effective communication between academics and industrial partners [32].

They are therefore an increasingly widespread solution around the world, valued for their role in supporting innovation, practice-oriented research and employability. Their structure and implementation differ from country to country, but they are all united by the aim of building a bridge between academia and industry through cooperation and practice-oriented doctoral education.

Joint doctoral theses of this kind prepared by people from the socio-economic environment also existed in Poland before the Implementation Doctorate Programme, but there was no nationwide, comprehensive programme with clear support from the ministry. This gives the programme many features that are unique worldwide. In programmes and projects in other countries, implementation is not an absolute requirement either (although it is naturally welcome), so there are works with a strong research component whose results may be used at some unspecified later date. Their scope — understandably — in neighbouring countries is limited to a single company that sponsors the costs of the work. The Polish Implementation Doctorate Programme is therefore increasingly cited as an exceptional model that covers all scientific disciplines and reflects the diversity of approaches used around the world [46, 47].

Before the launch of the Implementation Doctorate Programme, one of the factors holding back joint research and development projects between research institutions and companies was the lack of clearly defined opportunities for developing a research career on the basis of the results of implementation work. The introduction of the programme therefore increased interest in joint projects and made it easier to build good relations between universities and the business community. It also contributes to further improving the effectiveness of cooperation between science and the business environment, which is searching ever more intensively for top-class employees with exceptional skills and knowledge. More and more companies, as they develop their innovativeness, are also starting to devote more attention to research, development and implementation. This requires employees with knowledge of how to conduct research and the related practical skills.

The Implementation Doctorate Programme is an excellent response to this need. It also supports innovation processes and the commercialisation of the results of research and development work, as well as the related know-how, and promotes good practice in innovation. Thanks to it, the business environment has easier and wider access to researchers’ achievements, including patents and works that are intellectual property. Some of them were also created as a result of projects under the Implementation Doctorate Programme. This has made it possible to develop commercialisation activities and increase the innovativeness of companies. The cooperation itself and its promotion in society are also a good contribution to spreading knowledge about cooperation between science and business. This has not only helped to build and develop a knowledge-based society but has also encouraged young generations to deepen their skills and make friends with science.

The tangible outcome from the company’s (the cooperating entity’s) point of view is not only the doctorate itself, but also — perhaps above all — the implementation that has been achieved.

As mentioned, the Implementation Doctorate Programme in Poland is now almost ten years old, so — in order to assess how it works after these years — it was decided to launch a project aimed at an in-depth evaluation of the programme, in order to identify existing problems and embark on a path of continuous improvement. We decided to develop a model implementation doctorate with complete analytical data and input materials, and to use it to show the methodology of conducting an exemplary implementation doctorate. This methodology covers the entire process, from formulating the topic in line with the needs of the employing entity, through choosing a supervisor, to preparing the application. It also includes the Individual Research Plan, a very important element of the initial phase of the work, with which many doctoral candidates, supervisors and assistant supervisors, as well as doctoral schools — as the work of the advisory team to the Minister of Science and Higher Education

for evaluating applications and reports under the Implementation Doctorate Programme shows — have considerable difficulty. Next, annual reports containing all the elements necessary for evaluation by a reviewer were prepared for the selected project. The final result is the project’s final report. Reviewer evaluations were prepared for all reports, showing which aspects the advisory team pays attention to. The document prepared in this way is a model for all persons and entities interested in taking part in the programme, as well as for supervisors and doctoral schools supporting the development of the doctoral candidate’s competences.

The project concept rested on several pillars, the need for which follows from the conclusions drawn from the work of the experts of the advisory team to the Minister of Science and Higher Education for evaluating applications and reports under the Implementation Doctorate Programme.

The first pillar was the aforementioned creation of a model implementation doctorate covering all its stages, from the initial activities and the application through to the final report. The model was adapted to the requirements of different fields of science, which allows it to be distributed in all interested communities. The second pillar was a comprehensive analysis of the very rich data generated during the preparation of applications for implementation doctorates and the implementation of the projects themselves. These data cover, on the one hand, the fields and disciplines of science in which the projects are carried out and, on the other, the employing entities that decide to take them on. A comprehensive analysis was needed in terms of the size of the entities, industry, region, setting (large, medium and small town, rural area) and gender. Analysing the data in this respect made it possible to reach all the entities that can and should be interested in the programme.

And this is where the third pillar comes in, namely the broad dissemination of knowledge about the Implementation Doctorate Programme and encouraging initial steps to translate a business problem into a research topic with a strong implementation component. This is all the more important because the four-year process itself requires a great deal of commitment from the doctoral candidate, going well beyond their standard professional duties. The employing entity (the cooperating entity) should also prepare properly, not only by nominating a competent assistant supervisor and specifying the problem to be solved, but also by providing the necessary infrastructure and materials.

Awareness of the benefits gained is also important, including raising the level of the staff’s knowledge (alongside the doctoral candidate, the skills of their professional environment also grow) and the tangible effect of implementing innovation, which translates into market position, builds greater trust among business partners and — understandably — brings higher revenues. Dissemination activities included (and still include) scientific and popular publications and presentations at scientific conferences, seminars, workshops and symposia, including meetings dedicated to implementation doctorates.

Promotional activities comprised presentations at conferences and industry events and project meetings with companies and business representatives; information on the progress of the project, the results obtained and the goals achieved on websites; and popularisation of the project results and the knowledge gained on social media and via Radio Afera, the academic radio station of Poznan University of Technology, and the podcast “Polipodkast – człowiek, nauka, biznes” (Human, Science, Business). A dedicated promotional event (a science–business event) was also organised, at which the results of the project were presented (talks by the authors of the report and by representatives of employing entities, doctoral schools and doctoral candidates themselves). This report contains informational and educational promotional materials popularising the Implementation Doctorate Programme, which will be disseminated following a positive decision by the Ministry of Science and Higher Education.

During the project, the doctoral candidate is required to publish the results of their work regularly in scientific journals, which is not generally accepted practice in industry [48]. This is a very important element of the project, in which the supervisor should teach the doctoral candidate to prepare valuable publications. The rule adopted is that the future doctor, regardless of the scientific field they represent, should have the ability and the will to publish the achievements developed in their company for the advancement of knowledge in the given field.

It is worth referring here to the recent past. After the first five years of the Implementation Doctorate Programme, the report “Future Workforce – 5 Years of Implementation Doctorates” was published, prepared by Łukasiewicz Research Network [49]. Its aim was to summarise the results of the first five editions of the programme, assess the scale of its growth and show the impact of implementation doctorates on cooperation between science and the economy. The study shows the scale of the programme, its impact on the development of science–business cooperation and its role in building research and development staff for companies and public institutions in Poland. The report, whose most important elements we cite below together with our comments and remarks, presented data on the number of projects, their topics, the universities and companies involved, and the geographical distribution of the programme. Particular attention was paid to the role of implementation doctorates in building R&D staff for companies and in supporting the transfer of knowledge and innovation to the Polish economy. Analysing the content of the report, it could already be said with confidence at that time that the programme was a huge success for science, administration and business in Poland. In the first five editions of the programme, 2,293 implementation doctorate projects were approved, and a further 479 people qualified for the sixth edition. Detailed data are shown in the figure.

The programme developed dynamically despite the COVID-19 pandemic, which did not have even the slightest negative effect on the number of applications or projects. On the contrary — interest in participation grew in the following years. After these five years, implementation doctorates already accounted for more than 7% of all doctorates in Poland, which should be regarded as a significant success, all the more so given the relatively short time the programme had been running. The high durability of projects was also considered an important indicator of the programme’s effectiveness: for editions II–IV the share of projects still under way was 97%, 96% and 99% respectively, confirming the participants’ high stability and commitment.

Chart 1.1Number of accepted projects in successive editions

Accepted

Reserve list

386

463

372

591

481

I

II

III

IV

V

After the first five years, the gender split was such that men made up the larger group of participants (73.3% of projects), while the share of women was 26.7%. To some extent this resulted from the dominance of technical and engineering topics, traditionally chosen more often by men. And it was precisely this area, oriented towards the development of technology and industry, that dominated among the projects — and decisively so. As many as 63.7% of projects were in engineering and technical sciences, followed by natural and exact sciences (14.2%), social sciences (11.6%) and medical and health sciences (4.4%). These statistics are shown in the figure.

Chart 1.2Fields of science of the projects

Engineering and technical sciences

63.7%

Natural and exact sciences

14.2%

Social sciences

11.6%

Medical and health sciences

4.4%

Humanities

0.8%

Arts

0.2%

The most frequently represented disciplines included mechanical engineering, automation, electronics and electrical engineering, materials engineering, chemistry, and information and communication technology. Analysing the topics and keywords of the projects, we saw a strong focus on process optimisation, the development of new technologies, data analysis, artificial intelligence and machine learning, energy management and system security. This shows best that implementation doctorates are above all a tool for supporting the development of technology and industry.

Even then, the programme’s most important achievement was the successful building of R&D staff for companies. Almost 70% of implementation doctorates were carried out in the private sector, and of these almost 97% in companies. This made it clear that the programme effectively responded to the needs of business and supported the development of innovation in the economy. The most frequently represented industries at that time were machinery and electronics, the raw materials and metal industry, chemicals, IT, aviation and space, and pharmaceuticals and cosmetology.

Chart 1.3Main industries of companies participating in the programme

Machinery and electronics

10.3%

Raw materials and metal industry

10.1%

Chemicals and chemical products

7.4%

IT

6.5%

Aviation and space sector

6.1%

Pharmaceuticals and cosmetology

5.5%

Medicine and rehabilitation

5.2%

Automotive

4.8%

Land transport

4.2%

Energy

3.7%

The chart above shows how closely the whole idea was linked to the most innovative sectors of the economy. The programme also showed how interested large entities are in developing their staff. Among individual organisations, the largest numbers of projects were carried out by Łukasiewicz – Instytut Lotnictwa, PKN Orlen, Aptiv Services Poland, KGHM Polska Miedź and General Electric Company Polska. The clear leader in terms of the number of projects was Łukasiewicz Research Network, where doctoral candidates carried out as many as 189 projects, almost 10% of the total. The opportunity the Implementation Doctorate Programme has given these people therefore cannot be overestimated.

Interesting conclusions can also be drawn from an analysis of the programme’s regional significance. Although the largest number of projects were carried out in the largest academic and economic centres (Warszawa, Kraków, Wrocław, Katowice), the programme was clearly nationwide in character. Implementation doctorates were carried out in more than half of Polish counties and cities with county status. What is particularly interesting is that only 38% of projects were carried out in the same cities as the university running the doctorate, while as many as 61.6% were conducted outside the main academic centres. As is easy to see, this is the best proof of the effective transfer of knowledge and competences to smaller economic centres and of support for the development of local companies and institutions. The programme is not concentrated only in the largest cities — it supports the transfer of knowledge throughout Poland.

Map 1.1Implementation doctorates were carried out in more than half of Polish counties. Geographical distribution of projects — county map; the darker the colour, the more implementation doctorates.

more doctorates

fewer or none

Reading the report also led both its authors and us to some interesting key conclusions. Namely, after five years of operation, the Implementation Doctorate Programme proved to be an effective tool for supporting the innovativeness of the Polish economy. It successfully connected science with business, made it possible to build R&D staff for companies, actively supported the development of modern technologies, enabled the transfer of knowledge beyond the largest academic centres and, above all, responded to the real needs of companies and public institutions. The programme thus confirmed its great value as a mechanism supporting cooperation between science and the economy. It has given rise to competences, technologies and solutions that continue to strengthen the innovative potential of Polish companies and of the entire research and development system. Finally, it is worth adding that the authors of the report recommended further development and promotion of the programme, especially in the private sector, and a comprehensive evaluation once a larger number of projects had been completed. And this is exactly what our team undertook within the project Disseminating Best Practices in the Implementation of the Industrial Doctorate Program, funded under the Nauka dla Społeczeństwa programme.

Towards the end of our project, we received from MNiSW the report At the Interface of Science and Business – a Study of the Implementation Doctorate Programme. An Evaluation of the Programme’s Administrative Processes [50], prepared for the Ministry of Science and Higher Education by Ośrodek Przetwarzania Informacji – Państwowy Instytut Badawczy. The authors had much broader access to raw data, including data from applications and project reports; nevertheless, many of their observations are consistent with ours, and we present them below together with our comments and remarks. We have compiled detailed figures and present them in the fourth part of this publication.

The study conducted by OPI aimed at a comprehensive evaluation of the Implementation Doctorate Programme from the perspective of all the most important groups involved in its implementation. It focused on learning about the experiences of doctoral candidates, doctoral schools, cooperating entities, experts evaluating applications and reports, and ministry staff responsible for administering the programme. An important element was also identifying the problems, challenges, expectations and motivations of individual stakeholders. The process itself was carried out using several complementary methods, namely an analysis of data from the OSF system on the first seven editions of the programme, an analysis of the documentation and legal acts governing its operation, and qualitative and quantitative research, which made it possible to obtain a complete picture of how the programme works and to identify its strengths, organisational problems and areas requiring improvement. Individual in-depth interviews were conducted with doctoral candidates, supervisors, doctoral school coordinators, assistant supervisors and experts evaluating projects. In addition, a workshop was organised with MNiSW staff responsible for administering the programme. The online survey addressed to current and former participants asked, among other things, about their motivation for taking part, their sources of information about the programme, their level of satisfaction with its organisation and the results achieved through participation.

Looking at the programme in figures, in 2017–2023, covering the first seven editions of the Implementation Doctorate Programme, a total of 2,795 doctoral candidates qualified to take part, which places the programme among the most important instruments supporting cooperation between science and the economy in Poland. The largest group of participants were doctoral candidates carrying out projects in engineering and technical sciences, who accounted for 65% of all implementation doctorates. Natural and exact sciences (14%) and social sciences (11%) had much smaller shares. Other fields were represented less often: medical and health sciences accounted for 5%, agricultural and veterinary sciences for 4%, and the humanities for only 1% of all projects. The programme is therefore used above all to solve technological and engineering problems directly related to the activities of companies and institutions conducting R&D.

Among the participants there is also a clear predominance of men: in the period analysed they made up 71% of doctoral candidates, while women accounted for 29%. This structure reflects the dominance of technical and engineering fields, traditionally chosen more often by men. However, the share of women grew steadily — from around 24–30% in the first editions to 36% in the seventh. Women more often completed doctorates in medical, agricultural, veterinary and humanities fields, and much less often in technical sciences. Participants varied in age, but their profile differed from that of a typical doctoral candidate starting a research career straight after graduation. The median age was 32, so many participants already had several years of professional experience before starting their doctorate.

The youngest participant was 23 and the oldest as many as 77, which shows the programme’s great openness to people at different stages of their professional and academic careers. On the academic side, 80 research institutions located in 24 cities were involved in the programme. The vast majority were public universities (94%), while PAN institutes accounted for 4%, non-public universities for 2%, and research institutes had a marginal share (0.1%). These data show that public academic universities remain the main pillar of the programme. Cooperation with the socio-economic environment is an important element of the programme. More than 1,300 cooperating entities took part in the first seven editions, employing doctoral candidates while they carried out their research. Most of them represented the private sector (68%), while 29% were public sector entities.

This shows that the programme is above all a tool for supporting innovation in companies, although public institutions engaging in R&D are also playing an increasingly important role. The scale of the programme and the number of entities involved confirm its important role in building lasting links between science and the economy.

By the seventh edition, a total of 2,795 people had qualified. The largest number of participants was admitted in the fourth edition (584) and the smallest in the seventh (171), which was related to changes in the rules and a limit on the number of projects submitted by a single doctoral school. In addition, specialised modules were launched in subsequent editions, including on artificial intelligence, quantum technologies and metrology. The interviews show that implementation doctoral candidates are perceived as ambitious, motivated, hard-working and focused on the practical use of knowledge. Unlike doctoral candidates following the traditional academic path, they usually have richer professional experience, clearly defined research goals and a strong link between their research and their employer’s needs. At the same time, they more often have to reconcile research duties with their job and family life, which is an additional challenge for them.

The most important motivations for taking part in the programme were the desire to obtain a doctoral degree, the development of research competences, deepening specialist knowledge, pursuing their own research interests and the opportunity to solve a specific problem in the company. The possibility of simultaneous professional and academic development without having to give up work was also very important. For some participants the doctoral scholarship was another significant factor. Doctoral candidates expected above all to broaden their knowledge and competences, to increase their competitiveness on the labour market and to develop solutions that meet employers’ needs. They also hoped to develop research skills, gain experience in implementing research results in practice and establish new professional and academic contacts.

Younger participants more often had career-related expectations, while older doctoral candidates attached greater importance to solving practical problems and developing their network of contacts. For most participants, the programme was the only realistic route to a doctoral degree. More than two thirds of those who had not previously attempted a doctorate declared that without the programme they would not have decided to undertake doctoral studies. The programme’s key advantage over the traditional path was the opportunity to conduct research as part of their job, the employer’s support, access to research infrastructure and funding in the form of a scholarship. Before starting the programme, doctoral candidates often felt uncertain about whether they could combine work with a doctorate, about future cooperation with their supervisor and about the funding conditions.

During the programme, they most often pointed to being overloaded with duties, having to reconcile their job with the requirements of the doctoral school, the long wait for report evaluations and uncertainty about the continuity of funding. At the same time, they expected greater flexibility from doctoral schools, smoother communication and more predictable administrative procedures.

In most cases the initiative to join the programme arose from earlier cooperation between the doctoral candidate, the employer and the university. Applicants were often already employed in companies or research institutions and, together with their superiors, identified problems that could become the subject of a doctoral thesis. Supervisors and programme coordinators on the university side also played an important role, helping to refine the project concept and prepare the documentation. The recruitment process was usually assessed positively; it is particularly important (as we always stress) for all parties to develop a shared vision of the project beforehand. Finding an issue that meets both the scientific requirements and the company’s needs is not always easy. The topic must have adequate research value and at the same time lead to solving a specific practical problem and implementing the research results.

Preparing the application itself requires gathering a lot of information and presenting it correctly within a limited number of characters. The quality of the project’s preparation and the level of the partners’ commitment at this stage largely determine how the implementation doctorate will later proceed and how effective it will be. With successive editions, universities and companies gained experience, which made it easier to prepare applications. For a project to succeed, what matters greatly already at the application stage is the company’s real commitment, the employer’s willingness to provide time for research, access to research infrastructure and a good relationship between the doctoral candidate and the supervisor.

The project begins with establishing the relationship between the doctoral candidate, the supervisor and the assistant supervisor. This is where the difficulties of reconciling professional and family duties with the requirements of the doctoral school begin. At first it is hard to estimate the real workload required to carry out all these tasks simultaneously. The attitude of the cooperating entity (the employer) is important, as it can provide flexible working conditions, the possibility of conducting research during working hours and access to research infrastructure. The beginning of participation in the programme is crucial in this respect, although reconciling duties remains demanding throughout the project. Participants have to carry out part of their research outside working hours, often at the expense of their private time. If, in addition, the employer treats the doctorate as an employee’s side activity or even a whim, problems arise with completing the research on time.

Cooperation between the academic supervisor and the assistant supervisor also plays an important role. The supervisor is responsible for the scientific level of the work, while the assistant supervisor is responsible for its practical dimension and the possibility of implementing the results. During an implementation doctorate, participants must also fulfil the obligations arising from the doctoral school, such as attending classes and seminars, preparing scientific publications or completing professional placements. In the first editions, some doctoral school requirements were designed mainly with academic doctoral candidates in mind and did not always take into account the specific nature of an implementation doctorate (compulsory internships or placements in other entities); in subsequent editions we have seen a clear improvement here. It is therefore important that, usually at times when reporting deadlines, university obligations and intensive work tasks overlap, the doctoral candidate shows adequate mental resilience.

Another important element of the programme is the possibility of using funds earmarked for research infrastructure. For many projects this funding was important support, enabling the purchase of tools, equipment, software, research materials or services needed to conduct research. The procedures for spending the funds have also evolved over successive editions; in our view, the period of excessive formalities is now behind us (here, according to superstition, it is worth touching wood). Despite all these difficulties, participants stress that an implementation doctorate offers the opportunity to conduct research with a direct impact on the functioning of a company or institution, and that the results of the work are used in practice, which increases the sense of purpose of the research and the motivation to continue it.

Despite the many burdens, the programme is therefore seen as valuable, offering the opportunity to conduct research of real practical significance that combines scientific development with business practice.

During the project, participants prepare an annual report after each year, which from MNiSW’s point of view is one of the most important elements of progress monitoring. This process includes both a formal evaluation by the Ministry and a substantive evaluation by a team of experts (it was agreed internally that the expert who evaluated a given application remains responsible for the reports prepared under it). The evaluation of the report determines whether the year has been accepted and settled and whether it is justified to continue the project. The reporting stage is one of the most problematic elements of the programme because of the long wait for report evaluations. This wait (for reports not requiring corrections, the median waiting time is about half a year) causes uncertainty about further funding for the project and makes it difficult to plan the next stages of research.

If a report is returned for correction at the formal or substantive evaluation stage, the process takes even longer. Among the causes of this situation, we note in particular the limited staff resources at the Ministry, the quality of the reports submitted (some documents require corrections already at the formal evaluation stage, which means they must be processed again and prolongs the whole process), and changes to agreements and projects (a change of supervisor, company or topic), which make it necessary to prepare annexes and cause additional delays in evaluating reports. However — based on many years of experience working in the evaluation team — we do not believe that delays on the part of the experts evaluating reports are a significant cause. It rarely happens that the two-week deadline for evaluation is not met.

The reports themselves play a very important role as a tool for monitoring the progress of implementation doctoral candidates, requiring them to document the progress of their work systematically, plan further activities and maintain regular contact with their supervisor and assistant supervisor.

Finis coronat opus — every (well, almost every) project reaches its final phase: preparing the doctoral thesis and formal settlement. Doctoral candidates regard the period before submitting the final report as the most difficult, since most of their time was taken up by writing the thesis and publications and preparing for the defence. The final report itself, after the experience of three annual reports, was sometimes treated rather as a formality. However, the long wait for evaluation remains a problem — comments on the final report have sometimes arrived more than a year after the defence. Although the programme assumes a four-year implementation period, for many participants this proves insufficient to complete all research activities and prepare the doctoral thesis. According to OPI, more than half (51%) of the graduates who took part in the programme reported that they had used the option of extending the deadline for completing their doctorate.

This applied slightly more often to women. These results show that an implementation doctorate often requires more time than the programme’s standard schedule provides. Under the law, doctoral candidates employed outside the university are entitled to an additional 28 days of leave to prepare their doctoral thesis or prepare for the defence. More than half of the participants made use of this entitlement: 38% used the whole leave and 20% part of it. Leave was taken more often by women than by men, and more often by people employed in public than in private entities. The most frequently cited reasons for delays are the difficulty of simultaneously performing professional duties, conducting research and meeting the requirements of the doctoral school, as well as the longer time needed to carry out research, verify results or implement the solutions developed in the company.

Other reasons include the COVID-19 pandemic, which made access to infrastructure, raw materials and reagents difficult and disrupted the functioning of companies; the wish to refine the results; professional duties; lack of time; problems with publications, health or family situation; and various changes on the employer’s side. Despite these difficulties, however, most participants complete the programme successfully — among participants in the first three editions who have already finished the programme, as many as 85% completed it successfully, submitting a final report accepted by the Ministry. This is a huge success for the programme and certainly deserves wider publicity. Completing the programme brings tangible results both for doctoral candidates and for their organisations. These include the development of research competences, promotions, greater recognition as an expert, and the implementation of new technologies, products or organisational solutions in companies and institutions.

The remaining 15% of cases are withdrawals, which most often occur in the second and third years of the doctorate. The reasons are: a change in professional situation, problems with reconciling duties, loss of motivation, organisational difficulties or a change in life priorities. No significant differences in withdrawal rates were observed between women and men or between individual fields of science.

As mentioned, the benefits of participation do not end with the submission of the doctoral thesis or the end of funding, but often translate into participants’ further professional development, the development of organisations and continued cooperation between science and the economy. One of the programme’s most important effects is the growth of participants’ competences. Taking part in a doctorate makes it possible to develop research, analytical and project skills and to gain experience in conducting research and implementing its results in practice. For graduates, the doctorate has become an important element of their further careers, strengthening their position as experts and their promotion prospects both in academia and in companies. Participation in the programme also has a positive effect on relations between universities and cooperating entities.

The contacts established during the doctorate do not end with the defence of the thesis but lead to further joint R&D projects, expert cooperation or further implementation doctorates. The programme therefore serves as a mechanism for building lasting partnerships between science and the economy, which has been one of its goals from the start. The implementation outcomes are also important. A large proportion of projects have led to the development of new technologies, improved production processes, the implementation of new organisational solutions or an increase in companies’ R&D potential. The practical nature of the research is one of the programme’s greatest strengths, although not all implementation results are easy to capture immediately after the programme ends — sometimes the full effects only appear after several years, when the results are implemented on a larger scale or used in further projects.

This means that the programme’s real impact may be greater than current monitoring indicators suggest. The experience gained during the doctorate also changes the way cooperation between science and business is perceived — doctoral candidates, company representatives and university staff more often see opportunities for solving problems together and the benefits of conducting research geared towards practical applications.

The OPI report shows that the overall evaluation of the Implementation Doctorate Programme is clearly positive in all stakeholder groups surveyed. Among graduates, as many as 83% rate their experience positively, and the share of those dissatisfied does not exceed 4%. The benefits of the programme become particularly visible after completion, when participants can assess the results of their work from a broader perspective. The Implementation Doctorate Programme benefits all groups involved in its implementation — doctoral candidates, companies, universities and the whole system of science and the economy. For doctoral candidates, the greatest benefit is naturally the opportunity to obtain a doctoral degree without having to give up their job.

Many participants also cite as a benefit the financial support of the doctoral scholarship, which provides additional help while carrying out research, as well as the opportunity to work on problems of practical significance and to see the real effects of their own research. For companies, the programme is above all a way of developing the organisation’s R&D potential and gaining access to up-to-date scientific knowledge, research methods and academic competences that can be used to solve specific technological, organisational or product problems. Entities gain highly qualified specialists who remain in the organisation after the programme and use the experience gained in their further work. Another important benefit is the opportunity to implement innovative solutions and build a competitive advantage on the market.

For universities, the programme helps to strengthen cooperation with the socio-economic environment, makes it possible to establish new partnerships with companies and to develop existing R&D relationships. The programme has also made research more practical, strengthening the importance of applied research and the transfer of knowledge into practice. In a much broader perspective, the programme supports the building of lasting relations between the worlds of science and business, helping to break down barriers between these communities, making it easier to understand each other’s needs and creating conditions for long-term cooperation. The development of innovation and the transfer of knowledge and technology to the economy are also important. As a result, the programme achieves goals that go beyond the individual development of participants, contributing to the growth of the competitiveness and innovativeness of the Polish economy. It can therefore be said that the broad range of benefits for all participants means that the programme is regarded as one of the most important tools supporting cooperation between science and business in Poland.

Naturally, as in any programme, some areas requiring improvement can be identified here too, despite the very positive assessment of the programme’s idea itself. Everything should certainly be done to shorten the time taken to evaluate annual and final reports. This element probably lowers the assessment of the programme’s functioning the most. It is necessary to improve the quality of the reports submitted (this publication, among other things, is meant to contribute to this by showing model practice) and to increase staff resources at the Ministry. The latter task in particular is very important: at present a small team simultaneously handles many processes related to report evaluation, contract administration, project changes and communication with participants. It is also worth considering greater automation of administrative processes, including by developing the functionality of the OSF system, so that more processes can be handled electronically.

Linked to the staffing issue is the need to streamline communication channels, make information materials more accessible and provide training for doctoral school coordinators, experts and other programme participants, which would reduce the number of recurring questions and improve the flow of information. This publication is one of the important elements that will allow the working time of those running the programme to be used much more effectively. Doctoral schools will also benefit from this document, which should in turn reduce the very large number of project changes submitted. And while we are on the subject of schools, their greater flexibility in education and requirements should be continued — without, of course, compromising the doctorate itself.

Finally — as analyses and experience show — the monitoring of cooperating entities should continue, as there are situations in which companies do not provide doctoral candidates with adequate conditions for research or are not interested in implementing the project results. Of course, so as not to throw the baby out with the bathwater, formal requirements should not be multiplied; instead, solutions should be sought that encourage employers to provide conditions enabling the doctorate to be completed or — as a last resort — to withdraw from the project early. As can be seen, most of the recommendations do not concern the purpose, the assumptions or the nature of the programme, but the improvement of organisation, communication and administrative procedures.

Science is often associated with laboratories, complex formulas and scientific papers. Industry, in turn, is seen as a world of production, deadlines and everyday problems requiring quick solutions. The Implementation Doctorate Programme shows that these two worlds can complement each other and coexist in symbiosis. This is one of the reasons why it is regarded as a valuable and necessary tool supporting cooperation between science and the economy. The high level of satisfaction, especially among graduates, confirms the programme’s effectiveness in achieving its goals. At the same time, the results of the study point to the need for further improvement of selected organisational processes in order to increase its efficiency and the comfort of participants.

The Implementation Doctorate Programme, launched in 2017, is an instrument supporting cooperation between science and the socio-economic environment. Its essence is combining doctoral education with employment in a company or other organisation in which research is carried out to meet specific practical needs. The doctoral candidate thus operates simultaneously in the academic and professional environments, and the result of the work should be not only a doctoral thesis but also the implementation of the research results. The Polish model is in line with the concepts of industrial doctorates developed around the world, whose common goal is the transfer of knowledge, technology and competences between universities and the economy.

The programme has already reached a significant scale. In the first seven editions, covering 2017–2023, 2,795 doctoral candidates qualified, and the number of people who have studied or are studying within it now exceeds 3,000. The programme is clearly technical in character — around 65% of projects are in engineering and technical sciences. Men predominate among the participants (71%), although the share of women is gradually increasing. The median age is 32, indicating that the programme is often used by people who already have professional experience. The first seven editions involved 80 research institutions and more than 1,300 cooperating entities, with 68% of projects carried out in the private sector. Evaluation studies show that the programme’s main advantage is that it makes it possible to combine a professional career with academic development. For many participants, it is a path without which they would probably not have decided to do a doctorate.

Benefits are also gained by employers, who develop their employees’ competences, gain access to scientific knowledge and strengthen their own R&D potential, and by universities, for which the programme becomes a mechanism for building lasting cooperation with the economy. Implementation doctorates can lead to new technologies, products, process improvements and organisational solutions, and the relationships established during them often continue after the project ends. At the same time, the programme involves significant challenges. Doctoral candidates must reconcile professional duties, research, the requirements of the doctoral school and private life. The success of a project depends largely on the employer’s commitment, the provision of time and infrastructure for research and good cooperation between the doctoral candidate, the supervisor and the assistant supervisor. A particularly problematic element remains the long time taken to evaluate annual reports, resulting, among other things,

from the administrative burden, the quality of some of the documentation submitted and the number of changes that the Ministry has to handle. Delays in completing doctorates are also common, related, among other things, to the difficulty of reconciling professional and research duties, the course of the research, publications, and external and personal factors. Despite these problems, the overall evaluation of the programme is very positive. Graduates rate it particularly well — 83% of them report positive experiences of taking part. The high completion rate of projects also points to the durability of the programme. The most important recommendations therefore do not concern changing its basic assumptions but improving the way it operates: shortening the time taken to evaluate reports, reducing the administrative burden, increasing MNiSW’s staff resources, further developing the OSF system, improving communication and increasing the flexibility of doctoral schools.

The overall conclusion from the analyses to date is clear — the programme should be continued and developed, while its organisation and administration are improved at the same time.

One of the most important outcomes of a project carried out under the Implementation Doctorate Programme is the successful implementation of the solution developed. Unlike classic research projects, whose results are above all scientific publications or new theoretical concepts — sometimes with practical elements but without concrete implementation plans — projects in this programme are geared towards delivering measurable practical results. This means that the research results must be applied in the real operating environment of a company or institution, meeting specific, defined needs and — ultimately — helping to improve processes, increase efficiency or solve a specific technological, organisational or social problem.

Moreover, looking at this issue from the company’s point of view, it is becoming clearer year by year that the Implementation Doctorate Programme contributes to building competitive advantages in the economy, and that it gives employees of companies and public administration the chance to develop their professional careers while conducting research of real value to their organisation. Cooperation with a university, access to research facilities, the opportunity to consult experts and work on a topic with a direct impact on innovation in the company — these are the elements that make this path exceptionally attractive for employees and, at the same time, extremely beneficial for their employers. And employers pay much less attention to the science; what they are really interested in is a concrete implementation in their organisation and its effects.

To avoid a situation in which a project has no defined implementation, this chapter sets out to define implementation, to describe its character in different fields and disciplines, and to distinguish between what is and what is not an implementation. We present the assumptions and guidelines for implementing the solution developed, also analysing how it is implemented by the implementing entity. Particular attention is paid to the elements that confirm the practical application of the research results and their impact on the functioning of the organisation. The study is conceptual in nature, but it is based on the authors’ practical experience of supervising implementation doctorates.

Turning to the substance, the concept of implementation is widely used in research, applied projects and interdisciplinary activities. It appears in the technical and medical sciences as well as in the social sciences, the humanities and the arts. Despite its frequent use, the term implementation remains ambiguous, and its meaning depends on the disciplinary context. What one discipline regards as full implementation, another may treat as merely an intermediate stage. The lack of a clear understanding of the concept of implementation becomes particularly problematic in interdisciplinary research and in applied projects. Differences in the interpretation of the term affect how research is designed, how its goals are formulated and how its results are evaluated. As a consequence, this may lead to diverging expectations between researchers, stakeholders and funding institutions.

In individual fields of science, the concept of implementation often refers to entirely different activities and aspects. In the technical sciences it means putting a solution into operation in a working technical system; in the medical and pharmaceutical sciences — introducing a solution into clinical practice or onto the market; and in the humanities and social sciences — the practical application of research results in activities related to education, administration or culture. These differences are rarely defined, although they are important for assessing the maturity and usefulness of research results. The lack of a common, unambiguous conceptual framework makes it difficult to compare projects in different disciplines and to assess the extent of their implementation. This also applies — at least — to some interdisciplinary projects, especially where different fields of science are combined.

The aim of this chapter is also to propose an approach to the concept of implementation that is coherent and at the same time takes into account the specifics of each field. We present a universal definition of implementation based on the idea of applying research results in real systems specific to a given field. We then present interpretative variants of this concept in selected areas of science, together with a comparison showing the differences in implementation thresholds between disciplines. The final part discusses the implications of the proposed approach for interdisciplinary research and implementation projects.

In the scientific literature and in research practice, the concept of implementation functions as a seemingly intuitive but in fact ambiguous term [51]. Its meaning is rarely defined explicitly, and the way it is used depends heavily on the disciplinary context. As a result, the term is used very broadly: to describe the completion of development work, the launch of a solution and the achievement of real impact outside academia alike [52]. Such a range of meanings leads to conceptual ambiguity and makes it difficult to compare research results. One of the main sources of the conceptual problem is that implementation is often equated with, and confused with, other categories such as technical implementation, project delivery, commercialisation or the dissemination of research results [53]. These categories, however, describe different stages of a solution’s life cycle and relate to different criteria of its maturity. Technical implementation may, for example, mean that the solution has been built correctly according to specification, without determining whether it is actually used. Commercialisation, in turn, concerns bringing the solution to market, which is not always the goal of applied research in the public, social or cultural spheres.

acceptability, appropriateness, feasibility, adoption, fidelity, penetration into practice and sustainability [58]. In parallel, analytical frameworks are used to help identify barriers and facilitators of implementation (e.g. CFIR) [59], as well as frameworks for planning and evaluating interventions that take into account the individual and organisational levels (e.g. RE-AIM) [60]. An important complement is Normalization Process Theory, which explains the mechanisms by which new practices are “embedded” in the routine of an organisation [61]. A closely related, partly overlapping stream is research on translating knowledge and research into practice (knowledge translation and translational research). In this approach, implementation is understood as part of a broader cycle of moving from knowledge to action, as exemplified by the Knowledge-to-Action framework [62].

In addition, translational spectrum models set out the successive stages of moving from basic research to clinical practice and population impact, stressing that implementation thresholds change with the stage and type of target system [63]. In the social sciences and organisational research, implementation appears in the classic literature on the diffusion and adoption of innovation, in which implementation is a stage of the decision process leading to routine use of a solution. In public administration, the policy implementation stream is important: here implementation is understood as a distinct, complex stage of translating decisions into institutional practice, burdened with the risk of discrepancies between the intention of a policy and its actual execution. In technical and industrial fields, implementation is often linked to the assessment of technology maturity [64].

An example is the Technology Readiness Levels (TRL) scale, which indicates how ready a technology is for use in real conditions [65, 66]. In the humanities, social sciences and arts, by contrast, the emphasis is more often on the category of research impact and influence on the non-academic world, which is reflected, among other things, in evaluation practice. Models such as the Implementation Research Logic Model [67] point to the multidimensional nature of the benefits of research and to the need to analyse the mechanisms leading from scientific results to social and economic effects [68].

The conceptual problem of implementation becomes particularly apparent in interdisciplinary research [69]. Projects combining different disciplines often operate with a single term, “implementation”, even though individual project participants may attach different meanings to it [70]. As a consequence, implementation may be declared on the basis of criteria appropriate to one field, while from the perspective of another it remains incomplete or premature. The lack of a common conceptual framework then makes both communication within the research team and the evaluation of the project by reviewers from different areas of knowledge more difficult. The vagueness of the concept of implementation also has an institutional dimension. In the context of publicly funded projects and implementation doctorates, the lack of precise criteria leads to differing interpretations between applicants, funding institutions and evaluating bodies.

Implementation is then treated as a declared goal of the project, without a clear indication of the system in which, and the criteria by which, it is to be achieved [71]. At the same time, the literature rarely offers a synthetic, cross-cutting approach that would make it possible to compare implementation thresholds across disciplines and support communication in interdisciplinary projects. These problems point to the need to structure the concept of implementation in a way that, on the one hand, preserves its field-specific character and, on the other, makes it possible to compare and communicate the degree of implementation in interdisciplinary research. This gap motivates the proposal in this study of a unified yet field-aware definition of implementation, together with a comparative framework. Later in the chapter we also propose a universal approach to implementation and a set of interpretative variants of the concept in different areas of science.

The starting point for a universal definition of implementation is therefore the assumption that the common denominator of all forms of implementation is the application of research results in a real system specific to the given field. For the purposes of the discussion that follows, implementation can be defined as the process of practically applying research results, concepts or solutions in a real system specific to the given field, leading to their actual, lasting and responsible use, including outside the environment in which they were developed. The proposed definition emphasises several important elements that distinguish implementation from related concepts. First, implementation is understood as a process rather than a one-off event, which stresses its dynamic and staged nature. Second, the definition points to the need for practical application, not merely the formal completion of research or development work.

Third, a central role is played by the category of the real system, which makes it possible to capture the field-specific nature of implementation without reducing it to a single, universal technical criterion. Another important element of the definition is the requirement that the solution is actually used. This means that implementation does not occur when a solution has only been developed, installed or declared ready for use, but only when it is actually applied in the target system. This requirement makes it possible to distinguish implementation from technical implementation, a pilot or a test phase, which may — but need not — lead to the actual use of the specific solution developed. Another distinguishing feature of implementation is its durability. Implementation cannot be equated with a one-off application or a short-lived experiment; it assumes a certain degree of stability and repeatability of use.

Depending on the field, this durability may take different forms, from the continuous operation of a technical system, through a kind of routinisation of organisational practices, to the permanent presence of a work in cultural circulation. Finally, the definition stresses an aspect that could be described as responsibility for the consequences of use. Implementation implies taking responsibility for the consequences of using the solution in a real system, including its technical, social, health or cultural impact — here, too, this depends on the research area. This element distinguishes implementation from purely experimental activities and is particularly important in regulated fields and in research with potentially significant social consequences. Importantly, the proposed approach does not eliminate field-specific differences in the understanding of implementation, but provides a conceptual framework that makes it easy to compare them.

Implementation defined in this way may have different interpretative variants in selected areas of science, taking different forms depending on the type of real system and the criteria for considering the process complete.

Applying the universal definition of implementation proposed above requires, however, taking into account that in individual areas of science the concept refers to different types of real systems and to different conditions for closing the process. Let us now look at field-specific variants of implementation to illustrate how the same conceptual category can take on different operational meanings.

Let us begin with the area that seems most obvious, namely the technical and engineering sciences. Here implementation has a strictly defined, pragmatic meaning, clearly different from mere technical implementation or the publication of research results: it means applying a solution in a working technical system and operating it under operational conditions [72, 73]. It is thus the final result of the process of transferring the results of research or development work into practical use, in which the solution works in a real technical environment and meets user, production or operational requirements. The key technical criterion of implementation here is that the solution works outside the laboratory, in a real context of use (in a plant, system, network, production line or IT infrastructure).

It must also be operated (not just launched, but actually used), meet specific technical requirements and standards (safety, reliability, compatibility, scalability), and be repeatable and maintainable (capable of being maintained over a longer period, serviced and — depending on the situation — replicated, etc.) [74]. Prototypes, pilot installations or technology demonstrators may be stages preceding implementation, but as a rule they cannot be equated with it, unless of course the solution in question is actually used in the target system. If we analyse the successive stages of the research and implementation process — theoretical model, simulation, laboratory prototype, prototype in real conditions, installation in the operational environment and operation by the user — the threshold for implementation understood in this way is the prototype in real conditions.

Following this line of thought, we can determine what, for example, is and is not an implementation in individual engineering fields. In mechanical engineering, an implementation is, for example, a newly built machine or device working on a production line [75], based on a new pre-implementation design and a laboratory prototype. It is also a new component or item produced in series and working in a specific application, after the development of a new geometry and technology, a prototype made under research conditions (e.g. in a laboratory) and a prototype tested under near-production conditions. In automation, before implementation a control algorithm may be developed for a system simulation, followed by a system tested under field conditions as the threshold for implementation, i.e. a specific control system operating in an industrial installation (integration of the control algorithm or automation system with a real technical object).

Similarly in computer science: first an algorithm and code (or even a demo application) are developed, while the implementation will be a system running in a production environment and used by users, i.e. the actual launch of an IT system in a production environment and its actual use. Energy engineering will also begin with a model of the energy system and a test installation, followed by a pilot installation and, on that basis, connection (to the system) and operation of the solution in existing energy infrastructure — the implementation. Materials engineering will approach the subject analogously: a newly developed alloy and its laboratory samples will undergo application tests, followed by the actual use of the new material or materials technology in a real product (the implementation being precisely the material used in a product on the market).

Finally, environmental engineering, where a treatment concept, a pilot installation and a test phase lead to an implementation in the form of an installation operating at a wastewater treatment plant.

Let us now turn to the biological sciences. In this area implementation has a different character than in the classic engineering sciences, but it retains the same core: the transition from knowledge to practical application. The difference is that the “real system” is not a machine but an organism, a population or a biological process [76]. Implementation in the biological sciences therefore means applying the results of biological research in clinical, environmental, agricultural or industrial practice, leading to a measurable biological effect outside laboratory conditions. It may include the use of organisms, biological processes or biotechnological solutions on an environmental, production or clinical scale. The criterion of implementation here is achieving a measurable and repeatable biological effect in real conditions, taking into account biological variability and regulatory constraints (if any).

Again using examples, and without straying too far from environmental engineering: in ecology, the period before implementation will cover the population model and ecosystem simulations, up to a pilot programme as the threshold for implementing a working species or habitat protection programme. In molecular biology, research will include identifying a gene or metabolic pathway and in vitro tests, followed by pilot tests as the threshold, and we will regard the production of a protein or enzyme on an industrial scale as the implementation. Microbiology will first isolate a strain in the laboratory and carry out fermentation on a laboratory and pilot scale, and then implement a fermentation process operating under industrial conditions, i.e. the use of microorganisms or biological processes in production.

Applied biology for agriculture will deal with the selection of a genotype under greenhouse conditions and laboratory tests, followed by experimental plots as a prelude to implementing a specific variety grown by farmers or fruit growers, i.e. the application of the results of biological research in agricultural production.

Another field is food science. Here implementation has a meaning very close to that in the engineering and biological sciences, but with a key emphasis on food safety, production scale and, to some extent, consumer acceptance [77]. It is very clear that the product or technology must actually go into production and onto the market, not just work properly in the laboratory. Implementation therefore means introducing a new food product, ingredient, recipe or processing technology into industrial production and onto the market, in compliance with safety, quality and food law requirements, or the practical application of research results in the production or trade of food, leading to a safe and lawful product or technology being put into real market use. In this area, what matters in particular (apart from industrial scale and repeatability) is consumer health safety and compliance with food law.

To take an example from food processing: as in biology, we start with a recipe developed in the laboratory and technological trials up to pilot scale, after which the implementation is the launch of the technology in a real production plant and the resulting series production of food. If we consider bringing a new food product to market, the first stage will be the product concept itself, followed by a sensory prototype which, as the threshold, will have to pass consumer tests, and the implementation will mean placing the product on the market and making it available in retail. When analysing food safety, we will start by developing a method for detecting a pathogen in the laboratory and, through the validation of this method, arrive at the implementation, i.e. a way of carrying out routine quality control in the plant, as the application of safety assurance methods in production practice.

In food packaging and logistics, the successive phases are a new packaging material and its laboratory testing, storage-related tests and, as the implementation, packaging used in market distribution, i.e. the application of a new packaging or logistics solution in the supply chain. In this area, therefore, the following will not be an implementation: a recipe “on paper”, laboratory samples and the tests carried out on them and their interpretation, technological trials, or even a one-off trial batch without continuity (i.e. further batches), if the solution does not function in a real production and distribution chain. For the purposes of an implementation doctorate, of course, there is no need to demonstrate consumer acceptance; the risk in this respect lies entirely with the implementing entity.

In the medical sciences, implementation is much more formal and regulated in character and is closely linked to patient safety, clinical effectiveness and formal procedures [78]. It means introducing a specific medical solution (e.g. a therapy) into clinical practice or the healthcare system after meeting the requirements of efficacy and safety and obtaining the relevant institutional approvals. Clinical trials themselves, even at advanced stages, are not yet an implementation, which actually takes place only when the solution is used routinely in patient care. In pharmacy, in turn, implementation means bringing a medicinal product or pharmaceutical technology to the stage of marketing authorisation and its everyday use in accordance with the law. Implementation is thus closely linked to registration processes, quality control and post-authorisation surveillance.

Research and development work, validation or pilot production are stages preceding implementation but are not equated with it. Again using concrete examples: in the biomedical sciences — as in areas related to biology — the work will begin with the discovery of a biomarker and, through laboratory validation and preclinical tests, reach the clinical trial phase, and the concrete implementation will be a diagnostic test or therapy used in patients. Similarly, in clinical medicine (diagnostics and therapy), where the goal is the approval and use of a diagnostic or therapeutic method in patients, the starting point will be the discovery of a disease mechanism, followed by preclinical and clinical studies, and only their result — as the implementation — will be a method incorporated into treatment standards and used in practice.

When considering public health and the healthcare system, focused on applying research results in health policy or in the organisation of healthcare, the work will begin with an epidemiological analysis and expert recommendations, leading to a pilot programme, and a regional, national or international prevention programme will be regarded as the implementation. In pharmacy, where the end result should be bringing an active substance, medicinal product or pharmaceutical technology to the stage of marketing authorisation and routine use (the authorisation of a medicine and its use in therapy), after the identification of a molecule and in vitro, in vivo and clinical studies, the implementation phase covers a specific medicine that is registered and prescribed to patients. If we are talking about introducing a technology, the implementation will be the use of the manufacturing technology in industrial production.

The work as a whole should include laboratory-scale formulation, pilot stability studies, process validation and semi-industrial scale, ultimately leading to stable series production of medicines (e.g. tablets). Implementation may also be the application of pharmaceutical knowledge in clinical practice, where the development of guidelines and recommendations is followed by a pilot programme ending with the introduction of pharmacotherapy procedures used in a hospital. In this area it is most clearly visible that there is no such thing as “partial implementation” — either the method meets all the criteria or it is not implemented. Moreover, as a rule, introducing a research result into clinical practice or the healthcare system is possible only after its efficacy and safety have been demonstrated and the required regulatory approvals obtained. In this arrangement it is not the researcher’s decision but a systemic process — a decision of the regulatory body.

But implementation also exists outside the strictly material sciences. In public administration, it means the institutional introduction of regulations, public policies or organisational systems into the everyday practice of public bodies [79]. The key criterion of implementation here is not a product but the actual use of new solutions by institutions, offices or the state in general, and their availability to citizens. The mere adoption of a decision or the launch of an IT system (which perhaps nobody uses) is not an implementation if it does not lead to a real change in administrative practice. Implementation therefore means the practical introduction of a legal act, public policy, programme or organisational system into the everyday functioning of an institution, together with ensuring that it is applied by officials and citizens. Taking an example from law, the process begins with a new procedure or draft law, its adoption and the so-called

In this entire area of science, the condition for implementation is therefore not only a formal legal basis but also the readiness of institutions and, above all, application in practice and availability to citizens. Classic management, by contrast, regards as implementation the application of models, strategies or tools in a real organisation, as well as an actual change of processes in a company, regardless of its line of business [80]. The research will cover above all the creation and theoretical verification of the management model itself. It should be noted that the effect of implementation will be measured here much more by financial results from a business perspective than by theoretical correctness.

In the sport sciences, implementation has a practical and operational meaning: it is the direct translation of scientific knowledge into real training, organisational (the efficiency of the sport system) or rehabilitation activity [84]. The criterion of implementation is the actual use of methods, programmes or tools in the training cycle and the achievement of measurable sporting or health outcomes. The effect must therefore be applied in a real sporting environment, not merely described or tested in the laboratory. One-off tests or pilot interventions do not meet this criterion either. For example, in the theory and methodology of sports training, we will regard as implementation the real application of a training programme or method in the training cycle.

These will naturally be preceded by the research part, i.e. a theoretical model of exercise adaptation, a training programme “on paper” and pilot tests, and the result will be a specific programme carried out by the coach and athletes during the competition season. For physiology or biomechanics, implementation is the application of physiological or biomechanical knowledge in sporting practice. The work will begin with laboratory tests and biomechanical motion analysis, followed by specific training recommendations, and the result will be a change in running, jumping or throwing technique, etc., or in the athletes’ training load. The same applies to sport psychology — here implementation can be taken to mean the application of a psychological intervention in the real training or competition process.

This can start with a talent management model and the development of a training strategy, leading through a pilot in one section or club to a training system operating in a specific area or sports federation. The common denominator of implementation in sport is thus direct translation into practice and action in a real sporting environment and the durability of the solution; a measurable sporting or health effect is also important, as is the involvement of coaches and athletes. By contrast, laboratory tests without translation into training, recommendations that are not applied and — as in other sciences — one-off interventions without continuity are not implementations.

A great many activities and projects today are interdisciplinary in nature [85, 86]. Areas worth mentioning here include medtech, edtech, foodtech, govtech and art & science. It is very often here, at the boundary of two or more areas of science, that the most interesting inventions and implementations emerge. Rigidly assigning a project to just one specific discipline and never going beyond it is by no means positive or beneficial from a holistic point of view. Science as a whole knows no boundaries between its disciplines and fields; they have been defined — often quite artificially — by people. All interdisciplinarity and the ability to use knowledge from one area in another, sometimes quite distant, should therefore be seen very positively. Implementation in an interdisciplinary approach, following logical conjunction, means reaching the most restrictive implementation threshold among the fields combined.

Implementation combining a specific discipline with digital technologies is becoming increasingly common [87, 88]. For example, digital medicine will regard as implementation the launch and use of a digital medical technology in the healthcare system, for instance certified software used by doctors. Its starting point will be an artificial intelligence algorithm for diagnostic purposes, which will undergo technical validation and clinical studies. For digital administration (e-government), in turn, implementation will be the launch and real use of an IT system that is actually used by citizens and officials. Work in this area will begin with the design of the system and proceed through testing to technical launch. Similarly, in the rapidly developing field of digital art, implementation is a working interactive installation or online project, i.e. launching the work in a working technological environment.

Before this happens, there will be a concept, source code and a test version.

The conceptual framework of implementation proposed in this chapter has important implications for the design, conduct and evaluation of interdisciplinary and applied research. The key consequence of adopting a universal yet field-differentiated definition of implementation is the possibility of defining research goals and the criteria for achieving them more precisely in projects combining different areas of knowledge. In interdisciplinary research, the question of a shared understanding of implementation among project participants becomes particularly important. As shown in the previous section, individual disciplines use different criteria for considering a process implemented. Failure to agree on them explicitly may lead to a situation in which implementation is declared on the basis of criteria appropriate to one field, even though from the perspective of another it remains incomplete.

The proposed conceptual framework makes it possible to identify these differences at an early stage of the project and supports the development of a common language of communication. An important implication for interdisciplinary projects is the principle that implementation in such a project should be considered achieved only once the most restrictive implementation threshold among the fields involved has been met. This principle does not impose a uniform standard of implementation, but reflects responsibility for the functioning of the solution in the most demanding real system. Applying it can prevent implementation from being announced prematurely and make it easier to assess honestly how mature the research results are.

The proposed approach is also relevant to implementation projects, including implementation doctorates. A clear distinction between the stages of research, technical implementation, piloting and actual implementation makes it possible to define the doctoral candidate’s responsibilities and the real goals of the project more precisely. In particular, it makes it possible to separate scientific results from implementation outcomes without devaluing either dimension. From the perspective of funding institutions and evaluating bodies, the proposed framework can serve as a tool for more consistent evaluation of applied projects. Relating implementation to a specific real system and to field-specific criteria for recognising it allows expectations to be formulated more transparently and reduces the risk of differing interpretations between applicants and reviewers.

Finally, the proposed approach is relevant to communicating research results outside academia. Defining precisely what implementation consists of in a given context makes dialogue with external stakeholders — such as industrial partners, public institutions or social organisations — easier. In the longer term, this may encourage more realistic planning of implementation projects and increase trust in applied research.

This chapter has focused on structuring the concept of implementation which, despite its widespread use in research and development projects, remains ambiguous and highly dependent on the disciplinary context. The analysis showed that the lack of a clearly defined understanding of implementation can be a significant source of misunderstanding in interdisciplinary research and in projects oriented towards practical application — in other words, implementation projects. As a result, a universal definition of implementation was proposed, based on the premise of applying research results in a real system specific to the given field. This approach preserves the field-specific nature of implementation while making it possible to compare it across different areas of science. The variants presented for different areas of science show that implementation is not a uniform stage of the research process but a phenomenon with differing thresholds and criteria for recognition.

An important conclusion of this work is the need to consciously define the real system in which implementation is to take place and the criteria for considering the process complete. This is particularly important in interdisciplinary research, where the proposed principle of the most restrictive implementation threshold can be a useful tool for structuring research and evaluation expectations. The proposed conceptual framework can be applied both in the design and evaluation of applied research and in the context of implementation doctorates and other forms of practice-oriented research. Its value lies not in imposing a uniform definition of implementation, but in providing a common language that enables more precise communication between disciplines. Of course, the examples presented here do not exhaust the possibilities either of individual disciplines or of the interdisciplinary area. They can, however, serve as a guide to the premises for assessing implementation activity and its potential.

The implementation process is also important from the point of view of the durability of the project’s results. Incorporating the solution developed into the everyday activities of the organisation demonstrates its technological and organisational maturity and confirms that it can be used in the long term. At the same time, it is evidence of effective cooperation between the research institution and the implementing partner, which is one of the fundamental assumptions of the Implementation Doctorate Programme.

From the outset, the Implementation Doctorate Programme has covered a very wide range of projects in different fields and disciplines of science, sectors of the economy and types of organisation. It combines basic and applied research, development work and activities leading directly to the implementation of new technologies, products, services or organisational improvements. Such diversity means that individual doctorates differ significantly in the scope of research, the way cooperation is organised, the role of the business partner and the path leading to implementation outcomes.

An analysis of the experience gathered in successive editions of the programme shows, however, that despite this diversity, characteristic project patterns can be identified. Individual doctorates often follow a similar logic: they begin with the identification of a practical problem or a company need, then comprise the stages of research, development of a solution, its verification and implementation, and end with measurable benefits both for the business partner and for the development of scientific knowledge. The differences concern above all the nature of the problem, the way the research is conducted, the degree of the partners’ involvement and the type of expected results.

For this reason, four models of implementation doctorates have been developed, representing very different areas of activity. They describe imaginary cases, but are rather generalised reference models developed on the basis of an analysis of real projects and the experience above all of reviewers, but also of doctoral candidates, supervisors, entrepreneurs and institutions involved in the programme. The main aim of the models is to organise knowledge about how implementation doctorates are carried out and to create a practical tool to support the preparation of new projects. They can help future doctoral candidates better understand the whole process and plan the successive stages of work properly. For supervisors and company mentors, they make it easier to define the responsibilities of each party, plan cooperation and identify potential risks already at the project preparation stage. For entrepreneurs, the models show how to make effective use of the potential of cooperation with a research institution and how to link research activities to the real needs of the organisation.

The models presented can also be used in training, workshops and information activities on the Implementation Doctorate Programme. They can also serve as a point of reference in evaluating projects under preparation, facilitate the exchange of experience between programme participants and support the dissemination of good practice developed in successive editions. Their use increases the transparency of the implementation doctorate process, makes communication between all stakeholders easier and helps to build common standards for research and implementation projects.

It should be stressed that the models presented are neither a closed catalogue nor mandatory procedures. The Implementation Doctorate Programme remains flexible and allows the way a project is carried out to be adapted to the specifics of the scientific discipline, the company and the research problem. The models are auxiliary in nature: they show proven solutions, identify the key elements of the process and point to the relationships between the individual stages of the project. As a result, they can reduce the risk of organisational errors, make planning easier and increase the likelihood of successfully implementing research results in business practice.

One of the basic disciplines of engineering is mechanical engineering, which covers the design, analysis, manufacture and operation of machines and equipment. From the point of view of an industrial plant, its interests include design, manufacturing technologies and metrology — the three basic elements necessary for production. Metrology, the science of measurement, permeates practically all areas of mechanical engineering, supporting design, verifying technology and assessing the quality of workmanship in production. A particular area is the metrology of geometrical quantities, which deals with the measurement of shape, dimensions and surface texture, making it possible to assess whether real components correspond to design assumptions. In an age of advanced technologies such as the aerospace, automotive or medical industries, even minimal geometrical deviations can have serious consequences, which is why accurate measurements are essential at every stage of production and quality control.

Surface metrology is of particular importance: it deals with the analysis of the geometrical structure of surfaces, including roughness, waviness and topography. The surface of a component determines its functional properties, such as friction, wear, tightness or load-bearing capacity. In mechanical systems, for example, the right surface roughness affects durability and efficiency, while in medicine it affects the integration of implants with tissue. The development of modern measurement methods makes ever more accurate and comprehensive surface examination possible. This makes it possible not only to detect defects but also to optimise technological processes. Surface metrology therefore supports innovation, increases production efficiency and helps to raise quality standards.

Let us now imagine how an idea for a project in the Implementation Doctorate Programme comes about. For the purposes of the model, a hypothetical, non-existent company and university were invented. The people mentioned in the project are also purely hypothetical, and any resemblance to real persons or entities is coincidental and entirely unintentional.

Topo-Lab, located in a small town (about 40,000 inhabitants) in south-western Poland, is a medium-sized company producing instruments for measuring surface topography at the micro and nano scale. It tries to keep up with the latest trends in its industry, including the Industry 4.0 and Metrology 4.0 strategies. It produces instruments using non-contact techniques, analyses various competing solutions and follows the most modern trends using computed tomography. It developed its business on the basis of the automotive industry and the need to measure surface irregularities after plateau honing, without forgetting single-process surfaces and areal assessment in manufacturing processes. The company offers both hardware and control software for its instruments, which prepares the measurement conditions and calculates the values of surface topography parameters. It has developed its instruments with an awareness of measurement errors and reliability in surface irregularity measurements. The company decided to upgrade its instruments using artificial intelligence, which is ultimately meant to make the operator’s work easier and support the selection of measurement conditions and filters for specific applications. This approach requires research into various artificial intelligence techniques and the preparation of an implementation in the form of an algorithm and a program performing these tasks. The problem is therefore not trivial or easy to address, and there is no ready-made commercial solution on the market that could be bought and implemented. Such work was considered excellent material for a doctorate. It was therefore decided to join the Implementation Doctorate Programme and to find a doctoral candidate and a university to take on the topic. It is worth mentioning here that in Poland academic degrees (e.g. the doctorate) may be awarded only by universities, research institutes, institutes of the Polish Academy of Sciences and international research institutes operating in Poland that hold a sufficient scientific category (at least B+) in the given discipline. To simplify for the purposes of the example, the doctoral degree itself is awarded on the basis of a successful defence of the doctoral thesis, as proof of the candidate’s ability to conduct research independently. The subject of the doctoral thesis, in turn, is an original solution to a scientific problem or an original solution to the problem of applying research results in the socio-economic sphere.

After discussions, Topo-Lab decided to cooperate with a university — the Academy of Metrology, which has a well-functioning Doctoral School. Its staff include Professor Marek Berg, who has been working on surface topography measurement for years and also publishes on the use of artificial intelligence in the metrology of geometrical quantities. The company, for its part, proposed Stefan Nowak, MSc Eng., head of the Measurement Software Department, as assistant supervisor (an assistant supervisor does not need to hold a doctoral degree, but if they do not, they must have a significant record of implementation activity — implementations, patents — or at least five years of research experience). His department had recently hired a young mechanical engineer, Anna Kowal, MSc Eng., who is also interested in computer science and has expressed a wish to carry out research and implementation work. Together, the team prepared an application for an implementation doctorate.

*usually, at the application stage, the company has already chosen the doctoral candidate, but formally they do not appear in the application. They are added only after the application has been approved for funding by MNiSW and after successful admission to the Doctoral School. Only then are they formally registered in the project.

The Implementation Doctorate I module covers general doctoral topics, as opposed to special topics that the Ministry may define in a specific research area. No conflict of interest was found between the parties involved. The parties agree that the doctoral candidate will have access to research equipment at the university and to production and measuring equipment at the company. It was also agreed that intellectual property created as a result of the project will be managed as follows: if it is created on the company’s side, it will belong to the company; if it is created as a result of work at the university, it will belong to the university; if it is created as a result of joint work, the team will assess each party’s percentage share on a case-by-case basis. The university grants the company a right of first refusal on its share of the intellectual property.

The idea is then turned into an application based on the form created by the advisory team to the Minister of Science and Higher Education for evaluating applications and reports under the Implementation Doctorate Programme mentioned earlier. Each section of the application has a maximum number of characters — the aim being to keep applications concise and full of substance. The application describes the following elements in turn:

Description of the cooperating entity’s experience in cooperation with higher education, science or R&D entities, with particular emphasis on the last 5 years (up to 1,000 characters including spaces)

Topo-Lab has a Measurement Software Department employing 9 people, specialists in mechanical engineering and computer science. They develop control and measurement software, within which the department conducts R&D. The department cooperates with universities on projects: over the last 5 years it has completed 4 implementation-oriented research and development projects, including: Cybersecurity in measurement software, Calculation of surface irregularity parameters based on ISO 21920, Possibilities of using robust filters, and Elimination of spikes in surface topography signals. The company also takes part in a dual education programme at two technical universities in Poland. It provides placements for students from Poland and neighbouring countries. It conducts research with research institutes, including those of the Polish Academy of Sciences. The company also carries out projects under regional operational programmes related to the development of measurement techniques and employee competences.

The cooperating entity’s potential for carrying out the project (research equipment, industry experience, etc.) and information on the support offered by the entity employing the doctoral candidate, including a description of the assistant supervisor and their R&D experience (up to 1,500 characters including spaces).

Topo-Lab has been on the market since 1985 and employs 76 people. It has a range of machines for precision machining (milling machines, lathes, grinders) and 3D printers, enabling it to manufacture hardware components for measuring instruments. It also has an Electronics Department that designs and partly produces printed circuit boards. The Measurement Software Department is equipped with modern workstations and software for writing source code. The laboratory has measuring equipment for geometrical quantities, materials testing and electronics, including coordinate measuring machines, topography measuring instruments, a scanning electron microscope, a computed tomography scanner, a coating measurement system, a hardness tester and a universal testing machine. The assistant supervisor of the planned work will be Stefan Nowak, MSc Eng., a participant in many implementation-oriented projects with a background in computer science, who has specialised for 20 years in measurement and control software for measuring instruments (for the last 5 years as head of department). The company offers the doctoral candidate support with the implementation.

The aim of the doctorate is to analyse the possibilities of using artificial intelligence methods in surface metrology. This concerns, on the one hand, image analysis in optical measuring systems — determining the phase in measurement images by comparison with images previously generated on the basis of known wave phase shifts — and, on the other, the selection of the measurement technique based on detection parameters (measurement speed, sampling density, etc.) and data analysis stages (type and degree of data filtering). The work will lead to the development of a system supporting surface irregularity measurement. Part of this will be support in choosing the measurement technique — contact or non-contact. A more reliable selection of the measuring instrument will be possible on the basis of the implemented database. The algorithms and procedures created will make it possible to obtain unique research results on artificial intelligence methods and their use in surface metrology.

A maximum of seven (7) expressions separated by commas. Each expression may consist of several words.

Keywords: surface topography, measurements, control software, measurement software, artificial intelligence, filtering

Length and angle metrology is developing constantly. New measuring instruments are being created, together with new approaches to measurement algorithms and practical work. We are now using augmented or virtual reality and artificial intelligence. In the near future, the use of artificial intelligence methods in metrology will become a necessity. The costs of employing a worker and the time needed to prepare them to work on modern instruments are significant. The growing number of options in measurement software makes a good choice essential. Suggestions in this respect, or even just the existence of default options, will make the work much easier.

Over the years, a great many different types of instruments for measuring surface irregularities have been created. Support for the operator is very important, since even a change in lighting can produce completely different measurement results. The measurement process begins with entering data about the surface: how it was made, its function, material, etc. On this basis, the system can suggest a specific measurement technique or rule it out as unsuitable for the application. In the next step, measurement conditions relating to styli or objectives and lighting are prepared. Another important element is data filtering, to reveal the functional features of the surface, and the calculation of parameters. Measurement and data analysis systems currently in use do not have such capabilities. Modern measuring systems generate large data sets, whose analysis is becoming increasingly time-consuming. Implementing AI solutions in the analysis of large measurement data sets, obtained among other things with optical measuring systems, is today fully justified. In the era of the digitalisation of industry, solutions based on artificial intelligence and machine learning are a key driving force in the development of surface irregularity metrology worldwide.

The scope for implementing the results of the proposed research in practice is extremely wide. The goal of the project is to develop ready-to-use solutions for applying artificial intelligence methods, which can be used in various branches of the Polish and global economy. Surface irregularity measurements are used in a wide variety of applications, from the machine-building and mechanical industries, through automotive and aerospace, to the cosmetics, clothing and sports industries. In all applications in these areas, surface irregularities have an important functional significance, determining whether the products work properly.

At the same time, surface irregularity measurements are becoming increasingly complex and require ever greater knowledge from the operators of measuring instruments. On the one hand, we are increasingly keen to see 3D measurement as a valuable alternative to 2D measurement that provides much more information. On the other hand, the multitude of instrument designs using different physical principles makes this area of length and angle metrology difficult for many people to understand. Procedures related to measurement uncertainty are also the least understood here and far from implementation. To counteract this situation and ensure metrological reliability, artificial intelligence methods can be used. As a result, regardless of the branch of industry, it will be possible to make reliable surface irregularity measurements, regardless of how advanced the operator and the measuring instrument are.

The company is aware that abandoning development towards the use of artificial intelligence would ultimately lead to much lower customer interest in its products, which could eventually drive it out of the market. The implementation will take place in the company’s day-to-day operations, in the software it offers.

The research and implementation work planned in the project will be divided into 3 main areas, each of which is also a milestone. They are:

• —obtaining preliminary measurement data collected with contact and non-contact measuring systems,
• —analysing measurement data using procedures commonly used in metrology to assess the irregularities of the surfaces examined,
• —developing AI algorithms for analysing measurement data and supporting decisions on the selection of measurement parameters and technique.

Each area of research and implementation work will be carried out using a specific, differentiated research methodology. All areas will use the following research methods:

• —literature review (known AI solutions in surface metrology),
• —statistical methods of analysing measurement data, including the assessment of the basic parameters of surface metrology,
• —developing artificial intelligence algorithms based on supervised machine learning for data analysis and decision support in selecting measurement parameters and technique.
• —implementing the AI solutions developed in the existing metrology equipment infrastructure at Topo-Lab and at the university.

The risk analysis carried out at this stage makes it possible to identify threats in the following areas:

• —technical area – a medium risk was identified of failing to achieve satisfactory performance of the novel AI solutions developed for surface metrology.
• —external area – a risk was identified related to the availability of test objects with different degrees of surface irregularity — low level. This area also includes access to a wide range of contact and non-contact measuring systems — low level.
• —organisational area – a risk was identified mainly in connection with the scenario of the employment relationship ending during the doctorate. This risk is assessed as low.

Preliminary schedule of research and implementation work in the project

Scientific aspects (including publication plans) and aspects related to preparing the implementation must be clearly separated. Each semester should include both research activity and activity preparing for implementation. The version of the schedule in the form of the individual research plan (IRP), valid for the duration of the project, will be submitted in the report after the first year of the doctorate.

Three publications are also planned: after the second semester on artificial intelligence methods in surface irregularity metrology, after the fourth on feature selection and preparation for artificial intelligence, and after the sixth on the AI algorithm developed.

Description of the scientific or artistic competences of the academic supervisor in the subject area of the project, with a description of the supervisor’s scientific or artistic achievements in this area, with particular emphasis on the last 5 years (the description should include the most important publications and/or artistic works on the subject of the project) (up to 1,000 characters including spaces)

Prof. Marek Berg holds the position of professor at the Faculty of Geometrical Quantities of the Academy of Metrology. He is a specialist in length and angle metrology, with particular emphasis on surface irregularity metrology, coordinate measuring technology, computed tomography, nanometrology, reverse engineering and tribology. Over the last 5 years he has authored 2 books and 4 chapters, more than 20 journal articles and 12 congress and conference papers. In total he has more than 1,000 citations and an h-index of 15 (Scopus). He has reviewed 15 doctorates, 5 habilitations, 12 books, about 200 articles in Scopus-listed journals and more than 100 in other journals (about a third of these in the last 5 years). He is active in the Metrology Committee of the Polish Academy of Sciences.

Description of the academic supervisor’s experience in cooperation with the socio-economic environment, with particular emphasis on the subject of the project and the last 10 years (the description should include experience in supervising work, including doctoral theses, design, engineering, technological, implementation or artistic work, and R&D projects) (up to 1,000 characters including spaces).

Prof. Marek Berg has supervised 10 doctoral theses (including 5 by people from industry) and reviewed 15 (including 3 from industry) in length and angle metrology. He holds 4 patents in this area. He has led and taken part in 15 research projects, 5 implementation projects and 10 industrial implementation commissions in the metrology of geometrical quantities (most of them in the last 10 years). Expert of the National Centre for Research and Development. Most important recent publications (publications are listed here).

Information on adapting the doctoral school’s education programme to the assumptions of the Implementation Doctorate 2024 Programme (methods of working with the doctoral candidate, the possibility of combining the education programme with professional activity, alignment of the education schedule with the project tasks planned, the possibility of an individual study path, tutoring, crediting learning outcomes through exams/summer schools/certificates, choice of courses, reduced professional placement requirements, hybrid learning, etc.) (up to 500 characters including spaces)

The education programme comprises 600 hours over 4 years, which makes it easy to carry out the research tasks of an implementation doctorate. It also prepares doctoral candidates to achieve the basic learning outcomes. Classes at the Doctoral School take place in the afternoon, so they do not conflict with professional work, and the school also provides remote classes. Professional placements comprise 50 hours over the 4 years of study.

Information on the support offered to the doctoral candidate by the applicant, including the use of resources for the project (infrastructure, materials, methodological assistance, funding) (up to 500 characters including spaces)

The Academy of Metrology has measurement laboratories with a range of top-class instruments for surface topography measurement and machining. It also provides substantive, financial, IT and library support. It has specialists in many fields who will support the doctoral candidate in her research. The university’s experience is reflected in the national and international projects it conducts.

The application prepared in this way is evaluated by the advisory team for the Implementation Doctorate Programme. The individual evaluation criteria are discussed below; the precondition for evaluation is the implementation character of the project. If this criterion is not met, the application is rejected and not evaluated further. The reviewer therefore assesses:

Does the project have a clearly defined implementation character? (Yes/No)

After the research part, the project aims to implement a ready-made solution for a surface topography measurement application using artificial intelligence. It will make the operator’s work easier, supporting the selection of measurement conditions and filters. Since surface irregularity measurements are used in a wide variety of industries, the scope for implementing the results of the proposed research in practice is very wide.

Criterion 1.

Scientific or artistic achievements of the supervisor in the subject area of the project (Maximum points 15 / Points awarded 15)

Justification: The doctorate is being prepared in the discipline of mechanical engineering and concerns the metrology of geometrical quantities. The supervisor is a recognised specialist in length and angle metrology, including surface irregularity metrology, coordinate measuring technology, computed tomography, nanometrology, reverse engineering and tribology (i.e. the subject of the doctorate), not only in Poland but also abroad. He has numerous publications (more than 100 articles and as many conference presentations) and a good bibliometric record. Reviewer of many works, member of the Metrology Committee of the Polish Academy of Sciences.

Criterion 2.

The supervisor’s achievements in cooperation with the socio-economic environment (Maximum points 10 / Points awarded 10)

Justification: The academic supervisor readily cooperates with the socio-economic environment. He has taken part in many research and implementation projects and has also carried out industrial implementations as commissioned work. He also has experience as a supervisor and reviewer of doctoral theses.

Criterion 3.

Scientific or artistic significance of the project (Maximum points 15 / Points awarded 15)

Justification: The project relates to length and angle metrology, which is developing dynamically. Surface characterisation is an important aspect from both a functional and a manufacturing point of view. Procedures and solutions using artificial intelligence, which is itself the subject of intensive research, are increasingly used in metrology. All of this is present in the project, so its scientific value is high. The planned research will also contribute to the development of the discipline and lay the foundations for the use of AI in surface metrology.

Criterion 4.

Definition of the scientific problem and the implementation project, methodology of the project (Maximum points 20 / Points awarded 20)

Justification: The scientific problem has been defined correctly; it concerns artificial intelligence in the metrology of geometrical quantities, with particular emphasis on the micro and nano scale. The methodology starts with a literature review and proceeds through the preparation of research methods and samples to the development and implementation of a ready-made solution. The implementation proposed is software — not only for measurement, but supporting the user during measurement preparation and the steps leading to the result — using artificial intelligence procedures.

Criterion 5.

Scope of support for the doctoral candidate from the applicant and the cooperating entity — the future doctoral candidate’s employer (Maximum points 10 / Points awarded 10)

Justification: The applicant has measurement laboratories equipped with modern instruments and has declared that they can be used for work related to the doctorate. It also provides substantive support from specialists in many different fields and the necessary funding. The cooperating entity has also declared that its well-equipped laboratories and modern machinery can be used.

Criterion 6.

The applicant’s experience in implementation activity, with particular emphasis on previous participation in the Implementation Doctorate Programme (Maximum points 5 / Points awarded 5)

Justification: The Academy of Metrology has already taken part in the programme and fulfilled all its obligations very well and on time. The reports submitted were of an appropriate substantive and formal standard.

Criterion 7.

Impact of implementing the project on selected sectors of the economy or on society, including the significance of the implementation for the cooperating entity (Maximum points 15 / Points awarded 15)

Justification: Artificial intelligence is developing applications for practically every area of human life. Supporting day-to-day activities with its help seems indispensable, given the shortage of measurement staff and the increasing complexity and sophistication of measurement procedures. Filtering is a good example here. The company plans to implement the solution in its own production activities, which will allow it to strengthen its position on the domestic market and enter the international market more boldly. On this basis, the impact of the implementation can be assessed as high.

Criterion 8.

The cooperating entity’s experience in cooperation with higher education and science entities and the R&D sector (Maximum points 10 / Points awarded 10)

Justification: The cooperating entity, Topo-Lab, has experience of cooperating with universities — including technical universities with which it runs dual education and student placements. Together with the academic world it has completed 4 implementation projects. The entity also cooperates with research institutes.

Summary of the evaluation

Total (100 / 100)

Recommendation: Positive

Recommended for funding: Yes

Justification: A well-written project on a topic that is current and important for the development of science. An academic supervisor with a very good research record and a record of cooperation with the socio-economic environment. A very well-equipped university providing an appropriate level of education and staff supporting the doctoral process. A cooperating entity providing modern equipment and ready to implement the research results.

The Individual Research Plan (IRP) — as the name suggests — is a document with a certain degree of individuality. This means, among other things, that each Doctoral School has its own idea of it, and the differences between individual institutions — from our observations — are sometimes very large and distinct. Without judging this, we would simply like to point out that the IRP for a doctorate under the Implementation Doctorate Programme must include an implementation part. Below we present a sample Individual Research Plan containing the necessary elements — these may be extended, especially in the section on basic data about the student or the university. It is also worth being aware that at most universities individual sections may have character or word limits, and the IRP itself is prepared in both Polish and English.

Name: Anna Kowal

University: Academy of Metrology

Faculty: Length and Angle

Supervisor (academic supervisor): Marek Berg

Auxiliary supervisor: not applicable

Additional information: Assistant supervisor — Stefan Nowak, cooperating entity — Topo-Lab

Discipline: Mechanical engineering

a) thesis title

Surface topography supported by artificial intelligence

b) research hypothesis

Progress in measurement methods used in surface irregularity metrology increases the complexity of selecting the right technique and its parameters, which requires specialist knowledge and experience from the operator. The measurement result remains highly dependent on many variables. At the same time, the development of artificial intelligence methods makes it possible to develop effective decision-support tools, including systems that help define measurement conditions. It is therefore possible to create a system supporting surface topography measurement.

c) rationale for the choice of topic, research aim and methodology

The dynamic development of measurement methods used in surface irregularity metrology is making the selection of the right measurement technique and its conditions increasingly complex. In practice, this means that obtaining reliable results requires a high level of specialist knowledge and experience from the operator, and the measurement result itself depends on many interrelated factors. At the same time, the intensive development of artificial intelligence methods makes it possible to develop tools supporting decision-making in complex measurement tasks. In particular, it seems reasonable to use AI methods to support the operator in defining optimal measurement conditions in surface metrology.

The aim of the planned research is a thorough exploration of the possibilities of implementing artificial intelligence methods in surface metrology, the development of a database concept supporting AI solutions, an assessment of the relationship between surface functionality and measurement conditions for selected methods of surface preparation, and the development, implementation and deployment of an AI-based advisory system supporting the user in selecting the measurement method and its settings. These goals will be achieved in stages. In the first stage, samples will be prepared as the basis for building data sets for implementing machine learning algorithms. They will include both technological surfaces representing different manufacturing techniques and roughness standards. The prepared samples will then be measured using different measurement techniques — both contact and optical, allowing analysis in two and three dimensions — in order to determine the optimal measurement methods and parameters for each type of surface. The results will be processed into numerical data sets forming the basis for implementing artificial intelligence methods. These data will include information on the measurement method used, the characteristics of the surface (including type of machining, material and purpose) and a set of roughness parameters. The use of measurement data from Topo-Lab, the Academy of Metrology and other research institutions is also planned. The data collected will be subjected to statistical analysis to identify significant features describing the surface and the relationships between them. On this basis, using expert knowledge and the guidelines of standards, optimal measurement conditions will be determined for each measurement case, including the choice of technique, settings and roughness parameters. The next stage will be the development and implementation of artificial intelligence algorithms using available environments. As part of the implementation activities, the machine learning system will be trained on the prepared data set and will be used to select measurement conditions automatically for a given surface. Its performance will be verified on an independent test data set, which will make it possible to assess its effectiveness and estimate errors.

An important element of the work will also be the development of dedicated software implementing the artificial intelligence models in the form of a practical user tool, as an implementation task. The software will be designed as an advisory system allowing the user to enter data describing the surface under examination and to obtain recommendations on the choice of measurement method and its parameters. The solution is expected to be integrated with the existing measurement infrastructure and to be usable in laboratory and industrial conditions. The work will culminate in an analysis of the effectiveness of the solution and its implementation in the existing metrology infrastructure of Topo-Lab. The implementation will include both the implementation of the AI software and its validation under real measurement conditions, enabling the system to be used in practice as a tool supporting the user in the measurement process.

d) expected results and their significance and impact for science and society

The research will make it possible to achieve the scientific goals of the project, in particular the development of a comprehensive database of measurement data linked to recommended methods and conditions for carrying out measurements. In the course of the work, a set of experimental data will be collected, comprising the results of surface geometry measurements for different types of samples, obtained using many measurement techniques, together with an assessment of their suitability for specific applications. These data will form the basis for building a knowledge base that allows the right measurement method to be selected depending on the nature of the measurement task. One of the key results of the project will be the development of an expert system based on artificial intelligence. Using the database collected, this system will be trained through machine learning to select the optimal measurement method and its parameters automatically for a given surface and measurement purpose. Another important element will be the implementation of the algorithms in the form of dedicated user software, deployed at Topo-Lab with the possibility of licensing it to external entities. It will make it possible to significantly simplify measurement decision-making, shorten measurement preparation time and reduce the risk of errors resulting from an inappropriate choice of method or measurement parameters.

The significance of the proposed research for the development of science lies above all in integrating artificial intelligence methods with the field of metrology, which has so far made only limited use of such solutions. The database and AI models developed can form the basis for further research on the automation of measurement processes, the analysis of measurement uncertainty and the digitalisation of metrology. The project thus fits in with current trends in the development of intelligent measuring systems and the Industry 4.0 and 5.0 concepts. The project’s impact on the economy and society will be multidimensional. Surface irregularity measurements are used in many industrial sectors, such as machine building, automotive and aerospace, as well as in less obvious industries such as cosmetics, clothing and sports. In each of these fields, proper surface characterisation is crucial for the functionality, durability and quality of products. Making available a tool supporting the selection of measurement methods will help to increase the reliability of measurement results, improve product quality and optimise technological processes. In addition, given the growing complexity of measuring systems and the increasing competence requirements for operators, the use of artificial intelligence can significantly lower the barriers to entry into advanced metrology. The advisory system will make it possible to carry out metrologically correct measurements regardless of the user’s level of experience, which will make advanced measurement techniques more accessible. In the context of rising labour costs and the time needed to train specialists, solutions of this kind are an important support for industry. As a result of the project, it will be possible to create a tool that not only meets the current needs of surface metrology but also sets the direction for its further development, leading to greater automation, standardisation and digitalisation of measurement processes.

Dissemination will include the publication of three scientific articles in scored journals on the MNiSW list. Participation in two scientific conferences with posters or papers is also planned.

The doctoral thesis is planned to be submitted in September 2028.

Not applicable

The doctoral candidate is required to prepare a report on their work after each year of the project. Each report is evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. The documents presented below are examples and show essentially which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

The doctoral candidate’s report after year I

During the first year, all research and implementation tasks were completed. The research part was carried out at the university, and the implementation at the cooperating entity. Cooperation between the doctoral candidate and her supervisors went well and was based, among other things, on regular meetings and consultations.

In the research part, all planned research tasks were completed. A literature review of the applications of artificial intelligence in surface metrology was carried out. The analysis drew on, among others, the following sources:

1) Johnson, A., AI and surface filtering, Metrology in science, 23, 2025, 3487.

2) Stevenson, F., Berg, M., Surface analysis with machine learning procedures, Topography letters, 2, 2024, 6-18.

3) Makowski, J., Nakamoto, S., Enhancing metrology with IT, Journal of Internet Technology Applications, 7, 2024, paper 5512.

Analysing the literature, it is clear that in recent years artificial intelligence has become one of the most important tools developing surface metrology, in a way transforming it from a classic set of measurement techniques into an area of intensive data analysis. The review of the scientific literature shows that this change is not merely technological but concerns the very essence of what surface metrology is and what it is for. Traditionally, surface metrology focused on the precise measurement of topography and the determination of descriptive parameters such as Ra, Rz or Sa. These parameters were the basis for assessing surface quality in industry. However, with the development of measurement techniques — especially 3D methods — the amount of available data has increased significantly, revealing the limitations of the classic parametric approach. In this context, artificial intelligence is beginning to serve as a tool for analysing complex, extensive data sets (big data), shifting the centre of gravity from measurement itself to its interpretation.

One of the most important areas of application of AI in surface metrology is the classification and recognition of surface structures. Machine learning models, in particular convolutional neural networks (CNNs), are used to identify the technological processes responsible for creating a given surface, such as turning, milling or additive manufacturing. They are also used in defect detection — from microcracks to structural irregularities — and in surface segmentation, which allows local analysis of its properties. In many cases these methods outperform classic approaches, which rely on a limited number of statistical parameters and lose information about the spatial organisation of the structure. The literature clearly indicates that standard roughness parameters are insufficient for a full functional description of a surface. Different technological processes can lead to similar values of parameters such as Ra, even though their tribological, adhesive or lubricating properties are completely different. By analysing full 2D and 3D topography maps, artificial intelligence makes it possible to capture patterns and relationships that remain invisible to classic interpretation methods, extending the possibilities of interpretation. An interesting direction of research is the use of AI to predict the functional properties of surfaces. Models trained on topographic data are able to predict features such as the coefficient of friction, wear resistance or lubricant retention. This links surface metrology even more closely with tribology and materials engineering, creating “structure–function” models that are very practical in the design of machine parts. Artificial intelligence is also used at earlier stages of processing measurement data. The literature describes its use for noise filtering, separation of surface components (e.g. roughness and waviness), reconstruction of missing data (filling in unmeasured points) and improving measurement resolution. Here AI intervenes not only in the final analysis but also in the data preparation process itself, which further increases its importance in the entire metrology chain.

The literature also presents the limitations of the development of these methods. One of the main ones is — understandably — the availability of data. Unlike classic computer vision applications, surface metrology does not have large, publicly available data sets. The data available in the literature are often very limited, heterogeneous and dependent on a specific measurement method, which makes it difficult to compare results and to train universal models. The algorithm itself is therefore less of a problem than its training. Another issue for further development is the interpretability of models. In industrial applications, high predictive accuracy is not enough — it is also necessary to understand the mechanisms that lead to a given result. Deep learning models, although effective, often function as “black boxes”, which limits their acceptance in the engineering community. In response to this problem, explainable artificial intelligence (XAI) methods are being developed, as well as hybrid approaches combining physical models with machine learning. And it is precisely such hybrid models that are currently one of the most promising directions of development. Combining physical knowledge — e.g. contact models or wear theory — with the flexibility of AI makes it possible to obtain systems that are more reliable and generalise better. Rather than replacing classic models, artificial intelligence is beginning to act as their extension. At the same time, there is growing integration of AI with industrial systems within the Industry 4.0 and 5.0 concepts. Surface metrology (as part of Metrology 4.0 and 5.0) is ceasing to be an offline process and is becoming part of real-time quality monitoring systems, digital twins and predictive maintenance systems. As a result, surface analysis can be linked directly to the production process and its optimisation.

As the literature review shows, artificial intelligence therefore supports surface metrology while at the same time changing its character. This shift — from parameters to meaning — is the most important paradigm change currently taking place in the field. The full analysis is presented in the article: Kowal, A., Berg, M., Nowak S., Artificial Intelligence procedures in surface topography — a review, accepted for publication by Topography Letters (it is worth attaching the article to the report).

The work carried out in the current year of the project also defined requirements for solutions in individual measuring systems. They concern, on the one hand, classic aspects such as resolution, error and repeatability of measurement and, on the other, the system’s ability to generate data suitable for advanced algorithmic analysis and for integration with AI-based tools. In the traditional approach, the aim was to obtain reliable roughness parameters in line with standards. If, however, the data are to be used in machine learning models, it becomes important to ensure that they are representative, consistent and rich in information. This means taking into account features such as adequate sampling density, preservation of the full 3D topography and minimisation of measurement artefacts that could disrupt model training. Data standardisation is also important: different measuring systems — e.g. contact profilometers, optical interferometers or confocal microscopes — generate data with different properties (e.g. different lateral and vertical resolution, different noise characteristics). In the context of AI, a lack of consistency between data will lead to problems with the generalisation of the models developed. Requirements for measuring systems should therefore cover not only technical parameters but also the compatibility of data formats, filtering procedures and calibration methods, so that comparable, combinable data sets can be created. Some support here is provided by the ISO 25178 series of standards, which are aimed precisely at standardisation, regardless of the measurement technique used. Another important aspect is the quality of the measurement signal. In systems designed with AI analysis in mind, noise, systematic errors and missing data must be particularly limited. It would also be interesting to be able to record raw data, before any modifications, eliminations, filters or transformations are applied, since it is precisely such data that are often the most valuable material for learning models capable of identifying relevant features on their own. Unfortunately, practically no manufacturer of measuring equipment offers this option, as it is assumed that a customer seeing the raw data might not be convinced of the reliability of measurement with that particular system. In the context of industrial applications, requirements related to the integration of measuring systems with digital infrastructure are also becoming more important. These systems should allow automatic data processing, communication with databases and cooperation with production process control systems. In practice, this means providing appropriate interfaces, compatibility with Industry 4.0 platforms and the ability to work online or quasi-online. The interpretability and validation of results cannot be overlooked either. If data from the measuring system are to be used in AI models making technological decisions, the requirements should include traceability of data, assessment of measurement uncertainty and integration with explainable artificial intelligence methods. Finally, attention should be paid to the need for flexibility and scalability of measuring systems. AI models often require large and diverse data sets, which implies the need to design systems capable of working in different conditions, for different materials and technological processes. Requirements should therefore take into account the possibility of adapting measurement parameters and the ease of extending the system’s functionality. The analysis was also presented at the institution’s seminar.

In the second part of the work, test samples were prepared from objects with different surface irregularities. The irregularities were both more periodic and random in nature. Samples made of aluminium, bronze, brass, cast iron, carbon steel and austenitic steel were selected for the research, machined in various ways: rough face milling, finish face milling, grinding, turning, sandblasting, electrical discharge machining and plateau honing. Each combination of material and machining resulted from what was possible and appropriate to machine. For all samples, varying machining conditions were defined, particularly with regard to feed and cutting speed. In this way, 256 samples with different patterns and values of surface irregularity parameters were obtained. Example samples are shown in the figure.

The samples made in this way are the research material for further work.

In the implementation part, all planned tasks were also completed. The first preliminary algorithms for implementing AI solutions in surface metrology were developed. A concept was developed for a system for extracting measurement data (coordinates of measurement points) from the signals obtained from measuring systems. The essential element of this concept is the separation of information related to the instrument and measurement conditions from the data themselves. Its diagram is shown in the figure.

In this part of the work, existing programming languages and libraries were also analysed (list those analysed here). The AVX language was chosen because of its widespread use and availability, and because it meets the criteria set out in the research part. The cooperating entity provided access to computing machines and measuring instruments.

In the first year, the research materials for preparing machine learning databases were also analysed at the cooperating entity with a view to implementation. On the basis of data from the last 10 years, the basic materials that Topo-Lab’s customers work with and on which they measure surface irregularity parameters were identified. They include aluminium, bronze, brass, cast iron, carbon steel and austenitic steel. For these materials, the lower and upper limits of the expected variability of selected topography parameters (Sa, Sq, St, Sdq, Ssk, Sku, Sk, Spk, Svk) were determined, which in turn translated into machining parameters. For the set selected in this way, samples were made for the next tasks in the research part.

I assess the first year of cooperation with the doctoral candidate very positively. She is extremely committed to the subject, seeks answers to the research questions that interest her and is very hard-working. She attends consultations regularly and, as far as possible, also takes part in the academic life of the institution. She is very active at seminars and has presented the results of her work to date, which was received with appreciation by the team.

I confirm that the doctoral candidate has correctly completed the tasks related to the literature analysis of the applications of artificial intelligence in surface metrology. The literature review covers current research directions and clearly shows the growing role of AI methods in surface topography analysis. The doctoral candidate correctly identifies the main areas of application, such as the classification of surface structures, defect detection and the prediction of functional properties. She also shows an understanding of the change in the nature of surface metrology — from a parametric approach to one based on data analysis and functional interpretation. The research also identified and took into account significant limitations and challenges associated with the use of artificial intelligence, in particular the problem of data availability and model interpretability, which demonstrates a comprehensive approach to the subject.

I also confirm that, as part of the work, the doctoral candidate defined requirements for solutions in individual measuring systems. These requirements cover both classic metrological aspects, such as accuracy, resolution and repeatability of measurement, and elements arising from the use of artificial intelligence methods. In particular, they take into account the need to ensure adequate quality of measurement data, their representativeness, standardisation and the possibility of integration with digital systems and algorithmic analysis tools. Ms Kowal also pointed to the importance of issues such as the quality of the measurement signal, the possibility of analysing raw data, the interoperability of systems and their integration with Industry 4.0/5.0 concepts. The work undertaken confirms a proper understanding of both the theoretical and practical aspects of using artificial intelligence in surface metrology.

On the basis of the analysis and data from the cooperating entity, the doctoral candidate has defined a range of test samples corresponding to different materials and machining methods. These samples will be used in the following years to prepare the surface database.

In summary, I confirm that the doctoral candidate has completed the planned tasks correctly and that the report she has prepared provides a coherent and substantive basis for further research.

My impressions after the first year of supervising the doctoral candidate from Topo-Lab’s point of view are very positive. The doctoral candidate is creative and hard-working, often spending afternoons and evenings on our premises. She uses the resources made available to her and is keen to discuss scientific and implementation problems. She has become a valuable employee of the company, and her knowledge is increasingly appreciated by the whole team.

In the implementation part, the doctoral candidate completed all planned tasks in line with the assumptions adopted, achieving results of significant cognitive and applied importance, particularly from the point of view of our company — the cooperating entity. As part of the work, preliminary algorithms for implementing artificial intelligence solutions in surface metrology were developed. The proposed approach is an important step towards the practical use of AI methods in the analysis of measurement data, enabling their use in real industrial conditions. This is of particular importance for Topo-Lab, as our customers increasingly need to automate surface quality analysis and shorten the time needed to make technological decisions. Making the work of measuring system operators easier is also important.

The doctoral candidate also developed a concept for a measurement data extraction system, covering the acquisition of measurement point coordinates directly from the signals generated by measuring systems. The key element of this concept is the separation of information related to the properties of the instrument and the measurement conditions from the actual data describing the surface. This approach is of significant practical importance, as it makes it possible to increase the universality and comparability of data from the different measuring systems used in the company. As a consequence, it lays the foundations for building consistent databases and developing AI models capable of working in varied production conditions.

In the course of the work, the available programming languages and computing libraries were analysed for their suitability for implementing the solutions being developed. The choice of programming environment takes into account criteria related to performance, availability and the possibility of further developing the system. This is also of direct importance for our company, as it makes it possible to implement and scale the solutions developed in the existing IT infrastructure. On our side, it was also important to provide adequate technical infrastructure. We made computing resources and measuring equipment available, so that the solutions being developed are directly geared towards future implementation.

The results obtained are of particular importance for Topo-Lab in the context of developing modern quality control systems. The concepts and algorithms developed are expected to help increase the automation of measurement processes, improve the repeatability of surface assessment and make use of measurement data in decision-making. They are also an important step towards implementing solutions in line with the Industry 4.0/5.0 concept.

Another task completed by the doctoral candidate in the first year of the project was the selection of research material for preparing the surface database. On the basis of many conversations and analyses of our customers’ needs, materials and methods of subtractive machining were selected, for which appropriate samples were made. An impressive number of samples was obtained — 256 — which will be examined using various measurement techniques in the next stage.

In summary, I confirm the correct and comprehensive completion of the implementation part of the project. The results obtained have significant application potential and provide a solid basis for further development and future implementation of the solutions being developed in an industrial environment.

Name of entity: Academy of Metrology

Name of the doctoral candidate taking part in the programme: Anna Kowal

Project topic: Surface topography supported by artificial intelligence

Discipline(s) to which the project has been assigned: Engineering and technical sciences / mechanical engineering

Project period in the reporting year: full year / 12 months

Does the annual report under review confirm the implementation character of the project? yes

Does the annual report under review require a significant change or update of the IRP as a condition of its evaluation? no

Evaluation criteria

1. The doctoral candidate’s progress in the scientific discipline(s) related to the project topic; in the justification, indicate:

1.1. Knowledge and critical analysis of the literature and other sources related to the project topic: satisfactory

1.2. Publications on the project topic (e.g. scientific articles, monographs or chapters, scientific communications, working papers) prepared as a result of the doctoral candidate’s research: satisfactory

1.3. Activities aimed at obtaining patents for inventions, securing protection rights for utility models, and implementations related to or arising from the project: no information

1.4. Popularisation or promotion of the research results obtained in the project (e.g. conference presentations, participation in knowledge dissemination, grant applications, prizes, awards, workshops, summer schools): satisfactory

1.5. Originality and scientific potential of the project at the current stage: satisfactory

1.6. Substantive value of the academic supervisor’s opinion: satisfactory

Maximum points: 35

Points awarded: 35

Justification:

The doctoral candidate demonstrates knowledge and critical evaluation of the literature and other sources related to the project topic. She presented her achievements at the institution’s seminar. A publication will soon appear in Topography Letters, in which she is the first author. There are no other forms of popularisation yet, but at this stage of the project this raises no concerns. The project has good scientific potential. A very good and substantive opinion from the academic supervisor.

2. The doctoral candidate’s progress in research and implementation skills; in the justification, indicate:

2.1. Research methodology and methods and the ability to apply them to solve scientific, technological or design problems related to the project: satisfactory

2.2. Acquisition and use of the resources (e.g. information, data, materials, apparatus, equipment) needed for the project: satisfactory

2.3. Implementation potential of the project in the light of the results at the current stage of research: satisfactory

2.4. Clarity and editorial quality of the description of the doctoral candidate’s progress: satisfactory

Maximum points: 30

Points awarded: 30

Justification:

The doctoral candidate has correctly adopted and is applying the research methodology, and has the skills to apply it to solve scientific problems. She acquires resources correctly. The project has implementation potential that can be disseminated. A very good and clear description of the doctoral candidate’s progress.

3. The doctoral candidate’s progress in work related to implementing the results, taking into account the opinions of the supervisor and the assistant supervisor; in the justification, indicate:

3.1. Development of the doctoral candidate’s qualifications/competences necessary to develop the implementation version of the project in the reporting period: satisfactory

3.2. Use of the cooperating entity’s resources by the doctoral candidate to carry out the IRP in the reporting period: satisfactory

3.3. Involvement of the cooperating entity in the project: satisfactory

3.4. Substantive value of the assistant supervisor’s opinion: satisfactory

3.5. Originality and implementation potential of the work carried out in the project at the current stage: satisfactory

3.6. The cooperating entity’s potential to implement the project results: satisfactory

3.7. Likely impact of the doctoral candidate’s activities on the socio-economic environment: satisfactory

Maximum points: 30

Points awarded: 30

Justification:

Implementation qualifications are already developing at the current stage. The cooperating entity is interested in and committed to the project, and the doctoral candidate uses its resources properly. A very good and substantive opinion from the assistant supervisor. Satisfactory originality and implementation potential of the work carried out in the project. The entity certainly has good potential to implement the project results. The likely impact of the doctoral candidate’s activities on the socio-economic environment may be quite broad if the research results are made available.

4. Assessment of the progress of research and implementation work compared with the IRP; in the justification, indicate:

4.1. Is the IRP being carried out according to schedule? Yes

4.2. Assessment of the degree of completion of the IRP as at the date of the annual report: 25%

4.3. Have any threats to the completion of the IRP been identified? No

Justification:

The IRP is being carried out according to schedule. I assess the degree of completion at 25%. No threats to the completion of the IRP have been identified.

Total 100 out of 100

Summary of the evaluation

Does the annual report require additions? no

Is the report recommended for acceptance? yes

Should the project continue to be funded? yes

Reviewer’s summary opinion

Scientifically well-conducted work with very interesting implementation potential, which is already clearly visible and being developed at this stage. A good publication is in press; a very good, substantive description of progress and opinions from the supervisors.

This set of reports and the evaluation close the doctoral candidate’s first year. The evaluation of progress after the second and third years looks the same. After the fourth year, a final report is prepared — presented below together with the other necessary documents. One of its elements is a declaration that the doctoral thesis has been submitted, so at this stage it must be ready and submitted.

The doctoral thesis was submitted by the required deadline. A scan of the first page of the thesis is shown below.

During the doctorate, all research and implementation tasks were completed successfully. The research part was carried out at the university and the implementation at the cooperating entity, although the two types of work often overlapped very closely. A positive mid-term evaluation was obtained, which allowed the work to continue. Cooperation between the doctoral candidate and her supervisors went well and was based, among other things, on regular meetings and consultations.

As part of the research part of the project, a cycle of scientific work was carried out covering all the stages provided for in the schedule. The research was sequential and systemic in nature — from an analysis of the state of knowledge, through the preparation of experimental infrastructure and databases, to the development and validation of advanced artificial intelligence algorithms supporting surface metrology.

The first stage of work comprised a detailed literature review of the applications of artificial intelligence in surface metrology. This analysis made it possible to identify key development trends, of which the following proved particularly important: the use of deep learning methods to analyse surface topography, the development of approaches based on full 2D and 3D data, and the growing importance of predictive models linking surface structure with its functional properties. On this basis, the main limitations of existing solutions were also formulated, such as the lack of large, homogeneous data sets, problems with model interpretability and the limited possibility of implementing them in an industrial environment. In parallel with the literature review, requirements were defined for measuring systems intended to work with artificial intelligence algorithms. Unlike the classic approach, focused on determining parameters in line with standards, a perspective oriented towards the quality of input data for machine learning models was adopted. This meant ensuring high spatial resolution, data consistency, noise reduction and the ability to record the full surface topography. A significant problem of heterogeneity of data from different measuring systems was also identified, which made it necessary to develop procedures for standardising and integrating them. A set of test samples with varied geometrical structure and surface properties was also prepared.

These samples included surfaces produced by different technological processes, such as machining, abrasive processes and additive techniques. The aim of this stage was to obtain a representative data set enabling learning models to be trained on a wide range of cases. The preparation of the samples also took into account the possibility of measuring them repeatedly with different techniques, which was important for the subsequent comparative analysis.

The next stage involved selecting and analysing the measurement techniques used to acquire data. Both contact and non-contact methods were considered, with particular emphasis on optical measuring systems.

Their suitability for building databases for machine learning was assessed, analysing among other things resolution, noise characteristics, sampling density and susceptibility to measurement artefacts. The results of this analysis showed that different techniques generate data with different statistical and structural properties, which has a direct impact on model training and the models’ ability to generalise. The metrological features of the data obtained were also analysed. Example measurement results are shown in the figure.

In addition to classic roughness parameters, more advanced descriptors of surface structure were also considered, including areal parameters and representations based on full topography maps. It was shown that surfaces with similar values of parameters such as Sa can differ significantly in their functional properties, which confirmed the limitations of the classic parametric approach. An approach based on analysing full topographic data was therefore adopted as the basis for artificial intelligence methods. An important element of the work was the development of procedures for creating a database for machine learning. This process included integrating data from different sources, standardising, filtering and preparing them for analysis. Particular attention was paid to ensuring the representativeness of the data set, eliminating measurement artefacts and retaining information relevant to learning models. Procedures were also introduced to combine data from different measurement techniques in a way that allows them to be analysed together. This area — sometimes called data fusion — is entirely new, appearing only occasionally in publications and completely absent from practical software implementations.

The core stage of the research was the development of artificial intelligence algorithms for analysing large metrological data sets. Machine learning methods, including deep learning models, were used for tasks such as surface classification, identification of the technological process and defect detection. These models worked on full topographic data, which made it possible to capture complex spatial relationships. The results showed a clear advantage of these methods over classic approaches based on a limited number of parameters. They also made it possible to develop an algorithm supporting the selection of the measurement technique depending on the characteristics of the surface and the measurement parameters. This algorithm took into account, among other things, the type of surface, accuracy requirements and the degree of data filtering.

It is a step towards intelligent metrology systems capable of adapting automatically to measurement conditions and application requirements.

After the algorithm had been developed, the reliability of the models was assessed and they were validated. Tests were carried out using independent data, analysing the models’ effectiveness, stability and ability to generalise. Particular attention was paid to the problem of model interpretability, which is crucial in engineering applications. In this context, hybrid approaches combining physical models with artificial intelligence methods were also considered, which make it possible to increase the reliability and usefulness of the solutions developed.

The results show that the use of artificial intelligence in surface metrology leads to a significant paradigm shift in the field — from an approach based on analysing individual parameters towards the analysis of complex data structures and their functional significance. The integration of AI methods with measuring systems is in line with the development of the Industry 4.0 and 5.0 concepts, where metrology becomes part of real-time quality monitoring systems, digital twins and predictive maintenance systems. As planned, the research led to the development of a comprehensive approach to surface analysis using artificial intelligence, covering the entire data processing chain — from acquisition, through preparation and analysis, to the interpretation of results. The methods and algorithms developed form the basis of the doctoral thesis being prepared and will be used in modern metrology and industrial systems. They have been introduced into Topo-Lab’s software.

Three publications were published during the doctorate:

1) Kowal, A., Berg, M., Nowak S., Artificial Inteligence procedures in surface topography — a review, Topography Letters, 2, 4, 2025, 45-56.

2) Kowal, A., Berg, M., Nowak S., Svenson I., Algorithm to assist operator in surface topography analysis, Artificial Intelligence, 7, 2026, 32-46.

3) Kowal, A., Berg, M., Nowak S., Implementation of AI for surface topography analysis, Journal of Mechanical Engineering, 28, 2027, 1206-1230.

In the implementation part of the project, all planned tasks in the work schedule were also completed. The activities were iterative and integrative, covering both the development and implementation of IT tools and their gradual deployment in a real environment, using the cooperating entity’s infrastructure.

In the first stage of work, preliminary algorithms for implementing artificial intelligence solutions in surface metrology were developed. In parallel, the available programming languages and computing libraries were analysed, taking into account criteria such as computational performance, the availability of data processing tools and the possibility of integration with measuring systems. On this basis, a programming environment was chosen that provides adequate scalability and implementation flexibility. A concept was also developed for a measurement data extraction system, based on separating information related to the instrument and measurement conditions from the actual topographic data.

This approach was the foundation for further implementation work, making it possible to build universal data structures independent of a specific measurement technique. In the second stage, procedures were implemented for selecting the research materials used in creating machine learning databases. Using historical data from the activities of the cooperating entity (Topo-Lab), the most frequently examined materials and the ranges of variation of their surface parameters were analysed. On this basis, a representative set of materials was defined, including aluminium alloys, bronze, brass, cast iron and various grades of steel. For these materials, ranges of values of key topography parameters were defined, which made it possible to prepare samples corresponding to real industrial conditions and ensured high-quality input data for the artificial intelligence models.

The next stage involved implementing procedures for selecting the measurement techniques used in building databases. A methodology was developed for choosing appropriate measuring systems depending on the characteristics of the surface, taking into account both contact and non-contact techniques. As part of the implementation, it was made possible to integrate data from different instruments and unify them within a common data structure. An important element of this stage was the development of interfaces enabling automatic import and processing of measurement data, which significantly streamlined the creation of databases. Next, procedures were implemented for selecting the metrological features used in data analysis. In addition to classic roughness parameters, an extended set of descriptors of surface structure was introduced, including both statistical and areal parameters. The solutions developed made it possible to extract features automatically from measurement data and prepare them for further processing in machine learning models. This stage was an important link between the research and implementation parts of the project.

The next step was to implement a proprietary set of measurement parameters used in creating artificial intelligence databases (Fig. 7). These parameters were developed on the basis of earlier analyses and included both classic roughness measures and newly defined indicators describing complex surface features.

Implementing this set made it possible to standardise the data and increased their usefulness in model training. A preliminary implementation of the artificial intelligence algorithms developed was also carried out in the cooperating entity’s computing environment. The implementation included integrating the algorithms with the existing IT infrastructure and adapting them to work on real measurement data. At this stage, particular attention was paid to computational performance and the ability to process large data sets.

After this preparation, the algorithms were fully implemented, including their integration with measuring systems and databases. The solutions developed made it possible to analyse topographic data automatically, classify surfaces and support decisions on the choice of measurement technique. The system was designed in a modular way, which allows it to be extended further and adapted to different industrial applications. The implementation of the system in a Topo-Lab measuring instrument is shown in the figure.

The final stage of work involved validating the artificial intelligence algorithms implemented. Tests were carried out under near-real conditions, analysing the effectiveness of the system, its stability and its robustness to variability in input data. The validation results confirmed the correctness of the assumptions adopted and the high effectiveness of the solutions developed. Particular attention was paid to the compliance of the results with industrial requirements and the possibility of using the system in practice.

The results show that the implementation work led to the creation of a coherent system supporting surface analysis using artificial intelligence. The system covers the entire data processing chain — from acquisition and standardisation, through feature extraction, to analysis and interpretation of results. The integration of the solutions developed with industrial infrastructure is in line with the Industry 4.0 and 5.0 concept, enabling the automation of metrology processes and linking them to production management systems. The implementation part of the project thus led to the practical implementation of the methods and algorithms developed, forming a bridge between scientific research and its application in an industrial environment. The system developed can serve as the basis for further development of tools supporting surface metrology and their commercial use.

The doctoral candidate carried out the research in accordance with the schedule adopted. The work covers a full research cycle — from an analysis of the literature, through the preparation of research material and databases, to the development, implementation and validation of advanced artificial intelligence algorithms supporting surface metrology. In the literature review, the doctoral candidate did not limit herself to an overview of existing solutions but analysed them critically, identifying key directions of development such as the use of deep learning methods, the analysis of full 2D and 3D topographic data and the development of predictive models linking surface structure with its functional properties. At the same time, she aptly pointed out significant limitations of current approaches, including problems with data availability and homogeneity, model interpretability and implementation difficulties in an industrial environment. This shows that the doctoral candidate is very well acquainted with the current state of knowledge and able to formulate research problems of high scientific and practical importance. An important element of the work was also the proper definition of requirements for measuring systems working with artificial intelligence algorithms. The doctoral candidate adopted a modern perspective, focusing not on classic metrological parameters but on the quality of input data for machine learning models, taking into account aspects such as spatial resolution, data consistency and noise reduction. This approach should be regarded as particularly valuable in the context of the development of intelligent measuring systems. She also prepared a representative set of test samples, including surfaces produced with different technologies. Making it possible to measure them repeatedly with different methods provides a solid basis for further comparative analyses and for building reliable learning models. Another important achievement is the comprehensive analysis of measurement techniques and their impact on the properties of the data used in model training. The doctoral candidate demonstrated that different measurement methods generate data with different statistical and structural characteristics, which translates directly into the effectiveness and generalisation ability of artificial intelligence models. The move away from the classic parametric approach towards the analysis of full surface topography maps is particularly valuable and is confirmed by the results presented. She developed procedures for creating, integrating and standardising metrological databases. The introduction of methods for combining data from different measurement techniques (so-called data fusion) is a novel element, rarely encountered in previous work, both in publications and in implementations. This area has great potential for further development and is one of the significant contributions of the doctoral thesis. The use of machine learning methods, including deep learning, for tasks such as surface classification, identification of technological processes and defect detection made it possible to obtain results superior to classic approaches based on a limited set of parameters. The work has also produced an algorithm supporting the selection of the measurement technique, which is in line with the development of adaptive metrology systems. The models developed were validated using independent data, and their stability and ability to generalise were analysed. Attention was paid to the problem of model interpretability, and the inclusion of hybrid approaches combining physical models with artificial intelligence methods should be regarded as a particularly promising direction.

The doctoral thesis is modern and interdisciplinary, combining issues of metrology, data analysis and artificial intelligence. The research is in line with current trends related to the development of Industry 4.0 and 5.0, in particular in the context of intelligent measuring systems, digital twins and real-time quality monitoring systems. Another important asset is the application potential of the solutions developed, confirmed by their implementation in industrial software. In my assessment, the doctoral candidate shows very good research independence, the ability to carry out complex analyses and the ability to synthesise results and draw conclusions of scientific and practical importance. The doctoral thesis presented is very valuable, which also bodes very well for her future development. It is also worth noting the very good publications prepared during the research, to which the doctoral candidate made a major contribution.

The doctoral candidate carried out the research and implemented a ready-made solution in industrial practice at our company. She successfully translated the theoretical and algorithmic concepts developed into working application solutions, which is an important part of the achievements of the work. Particularly noteworthy is the comprehensive approach to the implementation process, covering the entire data chain, from acquisition from different measuring systems, through integration, to analysis using artificial intelligence methods and interpretation of results. This approach shows a very good knowledge of both the theoretical and practical aspects of implementing systems based on metrological data. A significant achievement is the implementation of procedures for integrating and fusing data from different measurement techniques. This solution addresses one of the key problems of modern surface metrology: the heterogeneity of data and the difficulty of analysing them together. The approach developed is innovative and has great application potential, especially in the context of building integrated measurement and analytical systems.

The artificial intelligence algorithms implemented, including machine learning and deep learning models, were adapted to work on real industrial data, which was a particular implementation challenge. Analytical methods were adapted to practical constraints such as data quality, the presence of noise and variability of measurement conditions. Full topographic data were used instead of a limited set of parameters, which allows more precise analysis and increases the usefulness of the system in industrial applications. It is also worth noting the development and implementation of an algorithm supporting the selection of the measurement technique depending on the characteristics of the surface and the measurement requirements. This solution is in line with the concept of intelligent, adaptive metrology systems and can be an important tool supporting decision-making in industrial practice.

A very important element of the implementation part is the actual implementation of the solutions developed in industrial software (Topo-Lab), which confirms their technological maturity and readiness for practical use. This implementation is clear proof that the results obtained are not merely theoretical but can be used effectively in real metrology systems. I also assess positively the attention paid to model validation and interpretability in the context of engineering applications. Implementing solutions based on artificial intelligence in industrial practice requires not only high effectiveness but also understandable results, which the doctoral candidate rightly recognised. The solutions presented are in line with current directions of development of modern industry, in particular the Industry 4.0 and 5.0 concepts, where the integration of measuring systems with data analysis and artificial intelligence methods plays a key role in ensuring quality, automation and predictive maintenance. The system developed can form part of more complex environments, such as digital twins or real-time monitoring systems.

The doctoral candidate has demonstrated high competence in transferring knowledge from research to industrial practice. This ability significantly increases the value of the entire doctoral thesis. I assess the whole cooperation with the doctoral candidate very positively.

Name of entity: Academy of Metrology

Name of the doctoral candidate taking part in the programme: Anna Kowal

Project topic: Surface topography supported by artificial intelligence

Discipline(s) to which the project has been assigned: Engineering and technical sciences / mechanical engineering

Does the final report under review confirm the implementation character of the project? Yes

Is the final report under review consistent with the IRP adopted? Yes

Evaluation criteria

1. The doctoral candidate’s activities in the scientific discipline(s) related to the project topic; in the justification, indicate:

1.1. Knowledge and critical analysis of the literature and other sources related to the project topic: satisfactory

1.2. Publications on the project topic (e.g. scientific articles, monographs or chapters, scientific communications, working papers) prepared as a result of the doctoral candidate’s research: (satisfactory / unsatisfactory)

satisfactory

1.3. Activities aimed at obtaining patents for inventions, securing protection rights for utility models, and implementations related to or arising from the project: documented

1.4. Popularisation or promotion of the research results obtained in the project (e.g. conference presentations, participation in knowledge dissemination, grant applications, prizes, awards, workshops, summer schools): satisfactory

1.5. Originality and scientific potential of the project from the perspective of its completion: satisfactory

Maximum points: 35

Points awarded: 35

Justification:

The doctoral candidate completed all the research tasks planned. She demonstrated knowledge of the literature, and the review raises no concerns. The project has original potential, and the research has contributed new scientific content. Certain elements may potentially qualify for protection rights. The results also include three publications in renowned journals — Topography Letters, Artificial Intelligence and the Journal of Mechanical Engineering — in which the doctoral candidate is the first author. The implementation has been documented. A very good and substantive opinion from the academic supervisor.

2. The doctoral candidate’s activities in research and implementation skills; in the justification, indicate:

2.1. Research methods and the ability to apply them to solve scientific, technological or design problems related to the project: satisfactory

2.2. Acquisition and use of the resources (e.g. information, data, materials, apparatus, equipment) needed for the project: satisfactory

2.3. Implementation potential of the project from the perspective of its completion: satisfactory

2.4. Clarity and editorial quality of the description in the report: satisfactory

Maximum points: 30

Points awarded: 30

Justification:

Throughout the doctorate, the doctoral candidate demonstrated knowledge of research methods and the ability to apply them to solve scientific and technological problems. She correctly acquired the resources needed for the project. I assess the implementation potential of the project from the perspective of its completion positively. The clarity and editorial quality of the description in the report are very good; she clearly writes with ease, which I naturally assess positively.

3. The doctoral candidate’s activities in work related to implementing the results, taking into account the opinions of the supervisor and the assistant supervisor; in the justification, indicate:

3.1. Development of the doctoral candidate’s qualifications/competences necessary to develop the implementation version of the project: satisfactory

3.2. Involvement of the cooperating entity in the project: satisfactory

3.3. Substantive value of the assistant supervisor’s opinion: satisfactory

3.4. Originality and implementation potential of the work carried out in the project: satisfactory

3.5. The cooperating entity’s potential to implement the project results: satisfactory

3.6. Likely impact of the doctoral candidate’s activities on the socio-economic environment: satisfactory

Maximum points: 30

Points awarded: 30

Justification:

I assess positively the development of the doctoral candidate’s qualifications and competences necessary to prepare the implementation version of a project carried out as an implementation doctorate. The same applies to the cooperating entity’s involvement in the project. The assistant supervisor’s opinion is very good. The project is original, with implementation potential. I assess positively not only the possibility of the cooperating entity implementing the project results, but also — perhaps even especially — the fact that the implementation has already taken place. The impact of the doctoral candidate’s activities on the socio-economic environment is considerable for everyone potentially interested in surface topography measurement.

Total 100 out of 100

Summary of the evaluation

Does the final report require additions? No

Is the report recommended for acceptance? Yes

Recommendation on the scope of additions, or acceptance or rejection of the final report: I recommend accepting the final report. The doctoral candidate achieved the goals of the work and brought about the implementation of the proposed solution at her workplace (the cooperating entity). She published three articles in renowned journals. The doctoral thesis is ready for defence.

This set of reports and the evaluation close the doctoral candidate’s entire period of activity. It also marks the end of the work of the expert from the advisory team for the Implementation Doctorate Programme on this particular project. The team does not evaluate doctoral theses; the appointment of reviewers and the rest of the procedure are the responsibility of the relevant Discipline Councils.

A very important area of the engineering and technical sciences is materials engineering, including foundry engineering, which covers the design, preparation and manufacture of metal products by pouring molten alloy into moulds. From the point of view of an industrial plant, its most important areas include alloy preparation, mould and core technology and casting quality control — the activities necessary to obtain products with the required functional and dimensional properties. An important role in this process is played by the control of technological and quality parameters, since foundry engineering is a field in which even small deviations in chemical composition, pouring temperature, solidification conditions or mould quality can significantly affect the final properties of the product. Proper supervision makes it possible not only to ensure that the process is correct but also to limit casting defects and increase production efficiency.

The quality of the surface and internal structure of castings is of particular importance, as they largely determine their durability, strength and suitability for service. In foundry practice, both the geometrical and dimensional assessment of the product and the analysis of its surface and any internal discontinuities, such as blowholes, shrinkage cavities, porosity or inclusions, are important. In modern automotive, machine-building and aerospace industries, castings must meet ever higher quality requirements, as they are often critical structural components. The development of modern foundry technologies, solidification simulation methods and modern testing techniques makes it possible to predict product quality more accurately and to optimise the entire production process. As a result, foundry engineering remains one of the key branches of industry, supporting the development of modern structures, increasing manufacturing efficiency and raising quality standards. In the context of foundry technology, methods that make it possible to analyse the relationships between technological process parameters and the quality of the materials obtained, including the identification of factors contributing to casting defects, are particularly important.

Below is a model example of a solution based on cooperation between a company and a university under the Implementation Doctorate Programme. For the purposes of the model, a hypothetical, non-existent company and university were invented. The people mentioned in the project are also purely hypothetical, and any resemblance to real persons or entities is coincidental and entirely unintentional.

CastMat Solutions, located in a small town (about 40,000 inhabitants) in south-western Poland, is a medium-sized company operating in metal casting and materials engineering. The company specialises in the design and manufacture of metal castings for the automotive and machine-building industries. In its activities, it tries to keep up with the latest trends in foundry technology and solutions related to the Industry 4.0 and Industry 5.0 concepts. The core of its business is the production of cast components from light metal alloys. These components are used in structural machine parts, engine subassemblies and other elements requiring adequate strength and operational reliability. In response to the growing quality requirements for industrial components, the company is developing its competences in material microstructure analysis, mechanical property testing and foundry process optimisation. It also places strong emphasis on quality control based on coordinate spatial scanning and data processing software. One of the most important technological challenges in casting production is ensuring highly repeatable material quality and limiting the occurrence of casting defects. These phenomena depend strongly on technological process parameters, which is why the company conducts R&D aimed at improving the mechanical properties of the castings obtained. Particular attention is paid to the possibilities of using modern methods of analysing production data and the results of materials testing.

In recent years, the company has become interested in using machine learning methods and artificial intelligence procedures to analyse process data. Its staff are considering the possibility of developing tools for predicting casting quality, in particular with regard to the occurrence of material defects, and linking it to technological process parameters. The rapid development of digital technologies and the growing availability of production data in industrial companies create new opportunities for analysing technological processes.

CastMat Solutions collects extensive data on the production process and the results of quality tests of the components it manufactures. These data are a potential source of information for identifying relationships between technological process parameters and the properties of the materials obtained. One of the key challenges, however, remains the development of methods for analysing these data in a way that allows reliable forecasting of casting quality and identification of the factors contributing to material defects. This gave rise to the idea of a research project aimed at developing analytical and predictive models for forecasting casting quality on the basis of process data. Such a project requires combining knowledge of foundry technology, materials engineering and data analysis methods.

Given the complexity of the issues analysed, covering elements of materials engineering, foundry technology and data analysis, the inadequacy of commercial casting process modelling software and the interdisciplinary nature of the problem, the company decided to cooperate with a technical university. This cooperation is intended to enable a research and implementation project under the Implementation Doctorate Programme, aimed at developing predictive models supporting the optimisation of foundry processes and improving the quality of the components manufactured. The expected result of the research would be tools supporting the optimisation of technological parameters and limiting the occurrence of casting defects in industrial production. The company also hopes to develop the competences of its staff, not only of the person running the project who wishes to obtain a doctoral degree.

After discussions, CastMat Solutions decided to cooperate with a university — the Technical University, which has a well-functioning Doctoral School. Its staff include Professor Marek Zieliński, who has been working on materials engineering for years, with particular emphasis on foundry engineering, and who also publishes on the use of artificial intelligence in materials engineering. The company, for its part, proposed Jan Sawicki, MSc Eng., head of the Research and Development Department, as assistant supervisor (an assistant supervisor does not need to hold a doctoral degree, but if they do not, they must have a significant record of implementation activity — implementations, patents — or at least five years of research experience). His department has for several years employed Roman Marzec, MSc Eng., a graduate of a technical university where he obtained a master’s degree in a programme assigned to the discipline of materials engineering. He is interested in further personal development and readily takes on research challenges. For this reason he was very happy to accept the proposal to take on a research topic that will result in an implementation at his home company. Together, the team prepared an application for an implementation doctorate (Table 1).

*usually, at the application stage, the company has already chosen the doctoral candidate, but formally they do not appear in the application. They are added only after the application has been approved for funding by MNiSW and after successful admission to the Doctoral School. Only then are they formally registered in the project.

The Implementation Doctorate I module covers general doctoral topics, as opposed to special topics that the Ministry may define in a specific research area. No conflict of interest was found between the parties involved. The parties agree that the doctoral candidate will have access to research equipment at the university and to production and measuring equipment at the company. It was also agreed that intellectual property created as a result of the project will be managed as follows: if it is created on the company’s side, it will belong to the company; if it is created as a result of work at the university, it will belong to the university; if it is created as a result of joint work, the team will assess each party’s percentage share on a case-by-case basis. The university grants the company a right of first refusal on its share of the intellectual property.

The idea is then turned into a formal application, prepared on the basis of the form developed by the advisory team to the Minister of Science and Higher Education for evaluating applications and reports under the Implementation Doctorate Programme. Each section of the form concerns aspects that are important from the point of view of the topic and the people and entities applying for the project. The form also has a maximum number of characters, to ensure that the information presented is concise and clear. It is possible to add an attachment if data need to be supplemented, but a good application can certainly be prepared without one. The elements described in the application are presented below.

1) Description of the entity’s experience in cooperation with higher education, science or R&D entities, with particular emphasis on the last 5 years (up to 1,000 characters including spaces)

CastMat Solutions has an R&D department employing 12 specialists in materials engineering, foundry technology and data analysis. The team develops methods for optimising foundry processes and analyses the properties of the materials used in casting production. The department carries out projects related to the analysis of the microstructure of metal alloys, the optimisation of solidification parameters and limiting the occurrence of casting defects. Over the last 5 years, the company has completed several R&D projects in cooperation with research institutions, concerning among other things the effect of casting process parameters on material microstructure and the improvement of foundry technologies. The results have improved the stability of the production process. The company also carries out regionally funded projects related to the development of casting production technology and raising employee competences. The company takes part in cooperation programmes with several technical universities.

2) The cooperating entity’s potential for carrying out the project — research equipment, industry experience, infrastructure (up to 1,500 characters including spaces)

CastMat Solutions was founded in 1994. It employs 118 people and has relatively modern technological facilities, including a production line for metal castings, a materials testing laboratory and a laboratory for measuring geometrical quantities. The laboratory is equipped, among other things, with instruments for analysing material microstructure, stations for testing mechanical properties and surface scanning and analysis systems. It also has software for statistical data analysis. The company also collects process data related to casting production, including technological parameters of the casting process, the chemical composition of alloys, solidification temperatures and quality control results. These data are an important basis for analysing the relationships between process parameters and material properties. Thanks to its technological facilities and experience in industrial production, the company has adequate potential to carry out R&D projects related to the optimisation of foundry processes.

The aim of the project is to develop methods for analysing process data and to build predictive models that make it possible to forecast casting quality on the basis of technological process parameters. The result will be tools supporting the optimisation of technological parameters and limiting the occurrence of casting defects in industrial production. The formation of non-conformities such as porosity, cracks or non-metallic inclusions depends strongly on technological process parameters. The company collects production data and materials test results, which will be used to identify relationships between process parameters and casting quality. The research problem consists in developing methods for analysing these data and creating predictive models for forecasting casting quality and identifying the factors contributing to material defects.

foundry engineering, casting defects, production process, quality, data analysis, prediction

5) Scientific significance of the project — state of knowledge, rationale for addressing the problem, significance of the project results for the development of the field and discipline (up to 2,000 characters including spaces)

The project is of significant scientific importance in materials engineering and foundry technology, as it concerns the analysis of the relationships between technological process parameters, material microstructure and the mechanical properties of castings. Understanding these relationships is one of the key issues in contemporary research on metallic materials and their manufacturing technologies. The project plans to develop methods for analysing process data and materials test results in order to identify the factors contributing to casting defects. Particular attention will be paid to the possibilities of using machine learning methods to model complex relationships between casting process parameters and the quality of the materials obtained. The project will contribute to the development of data analysis methods used in materials research and to deepening knowledge of the mechanisms by which material non-conformities form in casting processes. The research results may form the basis for new approaches to predicting material properties and identifying defects in manufacturing processes. The project is also in line with current directions of research related to the Industry 4.0 concept, in which the integration of materials engineering methods with advanced data analysis and artificial intelligence techniques is playing an increasingly important role.

6) Practical significance and implementation potential of the project — name and method of implementation, significance of the project for the cooperating entity and the Polish economy (up to 2,000 characters including spaces)

The project is of significant practical importance for CastMat Solutions and for the foundry industry. Developing methods for analysing production data and predictive models for forecasting casting quality will help to increase the stability of technological processes and improve the quality of the components manufactured. Implementing the project results in the company will allow earlier identification of the factors contributing to casting defects such as porosity, hot tears or non-metallic inclusions. This will make it possible to respond more quickly to unfavourable changes in process parameters and take corrective action while production is still under way. The use of predictive models can also support the optimisation of technological parameters such as pouring temperature, cooling conditions or alloy chemical composition. As a result, it will be possible to reduce the number of defective castings, cut material losses and lower production costs. Another important aspect of the project is the possibility of using the tools developed in production planning and technological decision support. Data analysis systems can form part of the company’s digital infrastructure in line with the Industry 4.0 concept, in which the integration of production data with advanced analytical methods plays a key role. From CastMat Solutions’ perspective, the project results can help to increase the company’s competitiveness by raising the quality of its products, improving the efficiency of production processes and developing its own R&D competences in modern foundry technologies. Implementing such a technology in Poland will also help to strengthen Poland’s role as a supplier of castings (cast components).

7) Concept and plan of research and implementation work — research plan and methodology, risk analysis, milestones (up to 2,500 characters including spaces)

The project concept involves developing methods for analysing process data and building predictive models for forecasting casting quality on the basis of technological process parameters. The first stage will involve an analysis of the current state of knowledge on the relationships between casting process parameters, material microstructure and the mechanical properties of castings. At this stage, the production data available in the company will also be analysed. The result (milestone) will be a set of baseline (production) data. The next stage will be the identification of key technological process parameters and the development of methods for preparing and processing production data (organising the data, preliminary statistical analysis and identifying relationships between process parameters and the occurrence of casting defects). The planned milestone is a set of process parameters and relationships. Later, models using machine learning methods will be developed (milestone). The final stage is the verification of the models under industrial conditions and an assessment of their usefulness in optimising technological parameters (final milestone). A number of potential risks have been identified in the project. The technological risks are:

a) insufficient data (especially on defects). High impact, low probability; a preliminary data audit and, if necessary, additional measurements as mitigation.

b) no clear relationships in the data. High impact, low probability; extending the set of variables and using expert knowledge (process engineers) as mitigation.

Organisational risks:

a) diverging expectations of the partners (company–science cooperation). Low impact, low probability; regular progress reviews as mitigation.

b) underestimating the time needed to prepare data. Low impact, low probability; an iterative approach and hybrid modelling as mitigation.

Personal and financial risks:

a) a change in the doctoral candidate’s life plans. Low impact, low probability; regular meetings with the supervisor and monitoring of the situation as mitigation.

b) a poor economic situation of the cooperating entity. Low impact, low probability; regular monitoring of the situation is planned as mitigation.

The research and implementation project is planned to last four years. During this time, successive stages of research are planned, covering a literature review, data preparation, the development of predictive models and their verification under industrial conditions.

Year 1

In the first year of the project, the research part will involve a detailed review of the scientific literature on foundry technology, the microstructure of cast materials and data analysis methods used in materials research. At this stage, the implementation part will analyse the production data available at CastMat Solutions, covering casting process parameters, the chemical composition of alloys and casting quality control results. The aim of this stage will be to identify the most important technological process parameters affecting material quality.

Year 2

In the research part, the second year of the project will be devoted to preparing and analysing process data and developing methods for processing them. Preliminary analytical models will be developed to assess the effect of individual technological parameters on material properties. In the implementation part, statistical analyses are planned, together with the identification of relationships between technological process parameters and the occurrence of casting defects, from the point of view of the entity’s machinery

Year 3

In the third year of the project, the research part will involve developing and refining predictive models using machine learning methods. These models will be used to forecast casting quality and identify the factors contributing to material defects. At this stage, the implementation part will involve testing the models developed on data from real production processes.

Year 4

In the research part, the final year of the project will cover modifications of the models based on tests under industrial conditions and an assessment of their usefulness in optimising technological parameters. In the implementation part, preparations are planned for implementing the analytical tools developed at CastMat Solutions. The final stage of the project will be the preparation of the doctoral thesis. At every stage of the doctorate, the publication of research results in scientific journals is also planned.

9) Scientific competences of the academic supervisor, with a description of scientific achievements in the subject area of the project, with particular emphasis on the last 5 years (up to 1,000 characters including spaces)

The academic supervisor of the project is Marek Zieliński, DSc Eng., associate professor at the Department of Foundry Engineering, Faculty of Materials Engineering, Technical University. His research focuses on foundry technology, the microstructure of metallic materials and the analysis of the properties of materials used in industry. His record includes more than 80 publications in indexed journals, concerning among other things the microstructure of iron and aluminium alloys, the mechanisms by which casting defects form and methods for optimising technological processes in foundry engineering. The results of his research have been presented at numerous scientific conferences. He has more than 600 citations and an h-index of 12. He has reviewed several doctorates and habilitations. He is active in the Polish Foundrymen’s Association. In recent years his research interests have also included machine learning techniques in materials research.

10) Description of the academic supervisor’s experience in cooperation with the socio-economic environment, with particular emphasis on the last 10 years (up to 1,000 characters including spaces)

Marek Zieliński, DSc Eng., has many years of experience in conducting research and R&D projects related to materials engineering and foundry technology. He has taken part in numerous nationally and EU-funded projects concerning the development of new structural materials, the improvement of foundry technologies and the analysis of the mechanical properties of castings for the automotive and machine-building industries. He has taken part in several projects carried out jointly with foundries and metal component manufacturers on the optimisation of foundry processes and material quality analysis, and has also carried out a number of commissions and expert opinions. He has served as supervisor and auxiliary supervisor in several doctoral proceedings and has supervised numerous master’s and engineering theses in materials engineering and manufacturing technology.

11) Information on adapting the doctoral school’s education programme to the assumptions of the Implementation Doctorate Programme (up to 500 characters including spaces)

The Technical University’s education programme comprises 600 hours over 4 years, which allows the research tasks to be carried out freely. Classes take place in the afternoons, on Fridays and Saturdays, and some can be taken remotely. The school uses flexible teaching methods, including an individual study path and tutoring.

12) Information on the support offered to the doctoral candidate by the applicant, including the use of resources for the project (up to 500 characters including spaces)

The Technical University has research laboratories equipped with modern apparatus and software related to the subject of the project. University staff (not only the academic supervisor) provide substantive consultations. The university has a modern library that also makes its resources available to doctoral candidates in digital form. It has extensive experience in carrying out projects, including under the Implementation Doctorate Programme.

Once submitted to the Ministry, the application is evaluated by the advisory team for the Implementation Doctorate Programme. The individual evaluation criteria are discussed below; the precondition for evaluation is the implementation character of the project. If this criterion is not met, the application is rejected and not evaluated further.

The reviewer therefore assesses:

Does the project have a clearly defined implementation character? (Yes/No)

The project aims to implement a solution supporting the prediction of casting quality and the identification of casting defects using machine learning methods. The tool developed will analyse process data related to casting technology, such as pouring temperature parameters, solidification conditions or alloy chemical composition, and then support technological decision-making in the company. The solution can be used in optimising technological parameters and in decision support systems in casting production. Given the wide use of foundry technologies in many industrial sectors, the potential scope for implementing the project results in practice is very wide.

Criterion 1 — Scientific achievements of the supervisor in the subject area of the project

The doctorate is being prepared in the discipline of materials engineering, and its subject concerns foundry technology and the analysis of the properties of metallic materials using data analysis methods. The supervisor, Marek Zieliński, DSc Eng., is a recognised specialist in the microstructure of metallic materials, foundry technology and the analysis of the properties of structural materials. He has an extensive research record, including numerous publications in Polish and international journals and conference presentations. His research concerns, among other things, the mechanisms by which casting defects form, the analysis of the microstructure of metal alloys and the optimisation of technological processes in casting production. The supervisor’s research record and his activity in the scientific community provide significant support for the project.

Criterion 2 — The supervisor’s achievements in cooperation with the socio-economic environment

The academic supervisor has extensive experience in carrying out R&D projects in cooperation with industrial companies. He has taken part in numerous projects concerning material quality analysis, the optimisation of technological processes and the implementation of new technological solutions in manufacturing companies. He also has experience in supervising doctoral work and cooperating with industrial entities, which is an important element in carrying out projects as implementation doctorates.

Criterion 3 — Scientific significance of the project

The project concerns foundry technology and the analysis of the properties of metallic materials, which are an important area of research in materials engineering. Understanding the relationships between technological process parameters and the microstructure and mechanical properties of materials is crucial for the development of modern manufacturing technologies. The project plans to use artificial intelligence methods to analyse process data and identify relationships between casting process parameters and the quality of the castings obtained. The use of machine learning methods in materials research is currently one of the fastest-developing research directions, which underlines the high scientific value of the proposed project.

Criterion 4 — Definition of the scientific problem and the implementation project, methodology of the project

The scientific problem has been defined correctly and concerns the development of methods for predicting casting quality on the basis of process data and materials test results. The research methodology includes a review of the scientific literature, the preparation and analysis of production data, the development of analytical and predictive models and their verification under industrial conditions. The implementation result proposed is an IT tool supporting the analysis of process data and the forecasting of casting quality. This tool will support the user in making technological decisions related to the optimisation of casting process parameters.

Criterion 5 — Scope of support for the doctoral candidate from the applicant and the cooperating entity

The applicant has research laboratories equipped with modern apparatus for analysing material properties and examining the microstructure of metal alloys, and has declared that they can be used during the project. The cooperating entity — CastMat Solutions — provides access to production infrastructure, materials testing laboratories and process data related to casting technology. The company also declares substantive support from specialists in foundry technology and data analysis.

Criterion 6 — The applicant’s experience in implementation activity

The research institution has experience in carrying out research and implementation projects and in taking part in the Implementation Doctorate Programme. The projects carried out so far have proceeded according to schedule and achieved the planned research and implementation results.

Criterion 7 — Impact of implementing the project on selected sectors of the economy or on society

Foundry technologies are used in many sectors of the economy, in particular in the automotive, machine-building and energy industries. Implementing tools that make it possible to predict casting quality can help to improve the efficiency of production processes and reduce material losses. CastMat Solutions plans to implement the solution developed in its production activities, which will improve the stability of technological processes and increase the company’s competitiveness on the domestic and international markets.

Criterion 8 — The cooperating entity’s experience in cooperation with research institutions

The cooperating entity — CastMat Solutions — has experience of cooperating with technical universities and research institutes. The cooperation includes R&D projects, student placements and joint initiatives related to the development of casting production technology.

Summary of the evaluation

Recommended for funding: Yes

The project is well prepared and concerns a topical subject related to the use of data analysis and artificial intelligence methods in foundry technology. The academic supervisor has a significant research record and experience of cooperation with industry. The research institution has adequate research infrastructure, while CastMat Solutions provides access to process data and an industrial environment enabling the project results to be implemented.

• —Name: Roman Marzec

University: Technical University

Faculty: Materials Engineering

Supervisor (academic supervisor): Marek Zieliński

Auxiliary supervisor: not applicable

Additional information: Assistant supervisor — Jan Sawicki, cooperating entity — CastMat Solutions

Discipline: Materials engineering

a) thesis title

Forecasting casting quality on the basis of technological process parameters

b) research hypothesis

The microstructure and mechanical properties of castings are the result of casting process parameters. With a large data set containing enough information to identify patterns associated with material defects, machine learning methods can be used to forecast casting quality effectively. The research hypothesis is that it is possible to develop reliable analytical and predictive models based on machine learning methods which, on the basis of process data and materials test results, will make it possible to forecast casting quality and identify the key technological parameters contributing to casting defects.

c) rationale for the choice of topic, research aim and methodology

The choice of research topic is justified both by current industrial challenges and by the directions of development of contemporary science and technology. The rapid development of the automotive and machine-building sectors brings growing requirements for the quality, reliability and repeatability of the material properties of components. In metal casting in particular, ensuring high casting quality while optimising production costs is one of the key technological challenges. The casting process is highly complex because of the large number of interrelated technological parameters, such as pouring temperature, alloy chemical composition, cooling conditions and mould parameters. The relationships between these parameters and the microstructure and mechanical properties of the material are often non-linear and difficult to describe unambiguously with classic analytical methods.

At the same time, the development of the Industry 4.0 and 5.0 concepts and the growing availability of production data in industrial companies create new opportunities for using advanced data analysis methods, including machine learning. CastMat Solutions collects extensive data on the course of the technological process and the results of quality tests of the components it manufactures, including microstructure analysis, porosity and mechanical properties. These data are a valuable but so far insufficiently used source of information that can serve to identify relationships between process parameters and the quality of the castings obtained. Research aimed at developing predictive models of casting quality is therefore justified both from a scientific perspective, related to the development of data analysis methods in materials engineering, and from an application perspective, arising from the possibility of implementing the results directly in the company. The interdisciplinary nature of the issue, combining elements of foundry technology, materials engineering and artificial intelligence, further justifies carrying out the research as an implementation doctorate, in cooperation between the company and a research institution.

The main aim of the research is to develop and verify analytical and predictive models using machine learning methods that make it possible to forecast casting quality and identify the key technological process parameters contributing to material defects. Achieving this aim requires achieving a number of specific objectives, beginning with an analysis of the state of knowledge on the relationships between casting process parameters, microstructure and the mechanical properties of materials. Next, it will be necessary to identify and characterise the production data and quality test results available in the company and to develop methods for preparing, cleaning and processing them. The next step will be to identify the key technological parameters affecting casting quality and to develop analytical and predictive models using machine learning methods. An important element of the research will also be the validation of the models under industrial conditions and an assessment of their usefulness in optimising technological parameters and improving production quality.

The research methodology will be interdisciplinary, covering both research methods specific to materials engineering and methods of data analysis and machine learning. In the first stage of work, a review of the scientific and technical literature and an analysis of the technological processes used in the company will be carried out. In parallel, an inventory of the available production data will be made, including an assessment of their quality, completeness and usefulness for further analysis. At this stage, methods of literature analysis, descriptive statistics and exploratory data analysis will be used. The next stage will cover data preparation and preliminary analysis. This work will include data cleaning — removing errors, filling in gaps, and standardising and transforming variables. Next, the relationships between technological process parameters and casting quality will be analysed using statistical methods. An important element of this stage will also be feature selection, making it possible to identify the parameters with the greatest influence on product quality. Later in the research, predictive models will be developed using selected machine learning algorithms. Depending on the nature of the problem analysed, regression and classification methods will be used. Where justified, the use of neural networks will also be considered. The models will be trained, and their effectiveness will be assessed using appropriate tools. An important stage of the research will be the validation of the models under real conditions. At this stage, tests will be carried out on industrial data to assess the models’ ability to forecast casting quality in production practice. At the same time, the interpretability of the models will be analysed, which will make it possible to identify the key technological factors contributing to defects. The final stage of the research will cover verifying the possibility of implementing the solutions developed at CastMat Solutions. Tests will be carried out under production conditions, together with an analysis of the effect of using the models on the quality of the castings produced and the stability of the technological process. The research results will be used to develop recommendations for optimising process parameters and implementing technological decision support tools. The project will contribute both to the development of scientific knowledge on the use of artificial intelligence methods in materials engineering and to increasing the company’s competitiveness by improving the quality and efficiency of production processes.

d) expected results and their significance and impact for science and society

The research project will produce a number of significant results of both a scientific and an applied nature. The most important expected results include the development of methods for analysing process data and the creation of analytical and predictive models that make it possible to forecast casting quality on the basis of technological process parameters. These models will make it possible to identify the key factors contributing to material defects and to determine the relationships between casting process parameters, material microstructure and mechanical properties. Another important result of the project will be the development of procedures for preparing and processing production data, which may be a universal solution applicable in other industrial companies. The research will also produce an organised, verified set of production data, which will form the basis for further analyses and for the development of technological decision support tools. If the models are verified positively under industrial conditions, they are expected to be implemented at CastMat Solutions as part of a decision support system enabling the optimisation of casting process parameters and a reduction in the number of material non-conformities.

The scientific significance of the project stems above all from its interdisciplinary nature, combining materials engineering, foundry technology and artificial intelligence methods. The results will contribute to the development of knowledge on modelling the relationships between technological process parameters and material properties, particularly in the context of using machine learning methods in materials engineering. The research results can be disseminated in scientific publications and form the basis for further research on the digitalisation of production processes and the development of intelligent decision support systems. The project also has significant applied and economic importance. Implementing the solutions developed in the company will make it possible to improve the quality of the castings produced, increase the repeatability of the technological process and reduce the number of rejects. This will in turn help to lower production costs, reduce raw material and energy consumption and increase the company’s competitiveness on the domestic and international markets. Developing employees’ competences in data analysis and the use of artificial intelligence methods will also increase the organisation’s innovativeness.

The project’s impact on society manifests itself above all in supporting the transformation of industry towards the Industry 4.0 and 5.0 concepts, which involve the integration of modern digital technologies with production processes. Reducing the number of defective products and using resources more efficiently will help to reduce the negative environmental impact of industrial production, in line with the principles of sustainable development. In addition, carrying out the project as an implementation doctorate fosters the development of highly qualified staff combining engineering competences with data analysis skills, which is particularly important given the growing needs of the labour market.

In summary, the expected results of the project include both the development of new analytical methods and tools and their practical application in an industrial environment, which represents a significant contribution to the development of science, the economy and a knowledge-based society.

The final stage of the project will be the preparation of the doctoral thesis. At every stage of the doctorate, the dissemination of research results in scientific journals is also planned.

Dissemination will include the publication of three scientific articles in scored journals on the MNiSW list. Participation in two scientific conferences with posters or papers is also planned.

The doctoral thesis is planned to be submitted in September 2028.

Not applicable.

The programme provides for annual reporting, so each doctoral candidate is required to prepare a report on their work after each successive year of the project. The report is evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

The first, second and third years of the doctoral candidate’s work end with a set of documents comprising the report and the supervisors’ opinions, and an evaluation of this material. After the fourth year, a final report is prepared — also presented at the end of the model prepared for mechanical engineering. Here, the declaration that the doctoral thesis has been submitted is particularly important.

The doctoral thesis was submitted by the required deadline. A scan of the first page of the thesis is shown below.

The final report is the last report prepared by the doctoral candidate and submitted together with the supervisors’ opinions. It covers the entire project (all years), not just the last year. This report is also evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

This set of reports and the evaluation close the doctoral candidate’s entire period of activity. It also marks the end of the work of the expert from the advisory team for the Implementation Doctorate Programme on this particular project. The team does not evaluate doctoral theses; the appointment of reviewers and the rest of the procedure are the responsibility of the relevant Discipline Councils.

Motion sickness (kinetosis) is a common health problem affecting a significant part of the population when travelling by various means of transport. Its pathophysiology is complex and linked to a conflict of sensory stimuli between the vestibular, visual and proprioceptive systems. Symptoms include nausea, dizziness, balance disorders and vomiting, which significantly reduce patients’ quality of life and limit their functioning in everyday and professional life. In recent years the problem has become more important with the development of new forms of transport and digital technologies such as virtual reality (VR) and simulators, which can also cause symptoms of kinetosis (so-called cybersickness). At the same time, there is a growing need for effective and well-tolerated methods of preventing and treating these disorders.

Existing therapeutic approaches include both pharmacological treatment (e.g. antihistamines, anticholinergics) and non-pharmacological methods such as adaptation training, behavioural techniques and vestibular rehabilitation. In recent years, modern therapeutic strategies have also been developing. They include the use of virtual environments for controlled exposure to symptom-provoking stimuli, training programmes aimed at vestibular adaptation, multimodal approaches combining different treatment methods and attempts to use neuromodulation methods to regulate the processing of sensory stimuli. Despite such efforts and significant scientific progress, the effectiveness of therapy remains variable and depends largely on individual patient characteristics such as vestibular sensitivity, susceptibility to sensory conflict, psychological factors and previous experience of exposure to symptom-provoking stimuli.

In clinical practice, the choice of therapy is still often empirical, and the lack of clearly defined principles for personalising treatment is a significant limitation on the effectiveness of therapy. There is a particularly noticeable shortage of tools for selecting the optimal combination of therapeutic methods for a specific patient. As a result, this is often done by trial and error, which naturally prolongs treatment and increases the risk of adverse effects. The contemporary development of data analysis and artificial intelligence methods creates new opportunities for personalising medical therapies. Collecting clinical, behavioural and environmental data makes it possible to identify relationships between patient characteristics and the effectiveness of specific treatment methods. In this context, solutions that make it possible to develop personalised therapy for motion sickness are particularly important. There is also a need to deepen knowledge of the factors determining the response to treatment and to develop a modern, individualised therapeutic approach that takes into account both classic and new methods of treating motion sickness.

Below is a model example of a research and implementation project under the Implementation Doctorate Programme, involving cooperation between a medical entity and a university. Hypothetical entities have been used for the model; any resemblance to real persons or organisations is coincidental and entirely unintentional.

The “MedTravel” Travel Medicine Centre, located in a large Polish city with a population of more than 200,000, specialises in the diagnosis and treatment of travel-related disorders, including motion sickness. The centre also conducts clinical activity and collects data on the course of patients’ treatment, including clinical information, lifestyle data and the effectiveness of the therapies used. The centre is interested in new technologies, including the use of data analysis and artificial intelligence methods. In recent years, the centre has noticed that the methods used to treat motion sickness vary in effectiveness. Clinical observations indicate that different groups of patients respond differently to pharmacological and non-pharmacological therapy. The possibility of developing tools for selecting therapy tailored to individual patient characteristics is therefore being considered.

The centre collects data on the course of patients’ treatment, including the characteristics of symptoms, triggering factors, the therapeutic methods used and an assessment of the effectiveness of treatment. These data can form the basis for a clinical analysis aimed at identifying relationships between patient characteristics and the effectiveness of therapy. One of the key challenges is to determine which clinical factors are most important for choosing effective treatment and whether it is possible to identify groups of patients requiring different therapeutic management. The company also has information on patients, including demographic data, history of motion sickness and the occurrence of adverse effects. These data, in turn, can be a potential source of information for identifying relationships between patient characteristics and the effectiveness of therapy. This gave rise to the idea of a research project aimed at identifying the factors affecting the effectiveness of motion sickness therapy and developing principles of personalised therapeutic management. Given the interdisciplinary nature of the problem, covering elements of the medical sciences, data analysis and artificial intelligence, it was decided to cooperate with a medical university. The scientific partner of the project is the Medical University, where Professor Joanna Szymańska works, specialising in neurology and transport medicine, as well as in the use of data analysis methods in medicine. The cooperating entity proposed Piotr Majchrzak, MD, PhD, as assistant supervisor; he has more than ten years of clinical experience in treating patients with motion sickness (an assistant supervisor does not need to hold a doctoral degree, but if they do not, they must have a significant record of implementation activity — implementations, patents — or at least five years of research experience). Dr Majchrzak has also taken part in projects related to the use of specific medical preparations reducing travel discomfort. The doctoral candidate is Marta Stachowiak, a graduate in medicine who, apart from medicine, is also interested in information technology. Together, the team prepared the concept of a research and implementation project, which will result in an application submitted under the Implementation Doctorate Programme.

*usually, at the application stage, the company has already chosen the doctoral candidate, but formally they do not appear in the application. They are added only after the application has been approved for funding by MNiSW and after successful admission to the Doctoral School. Only then are they formally registered in the project.

No conflict of interest was found between the parties involved. The parties agree that the doctoral candidate will have access to research equipment at the university and to patients, medicines and infrastructure at the company. It was also agreed that intellectual property created as a result of the project will be managed as follows: if it is created on the company’s side, it will belong to the company; if it is created as a result of work at the university, it will belong to the university; if it is created as a result of joint work, the team will assess each party’s percentage share on a case-by-case basis. The university grants the company a right of first refusal on its share of the intellectual property.

The concept is then translated into a formal application, prepared on the basis of the form developed by the advisory team to the Minister of Science and Higher Education mentioned earlier, responsible for evaluating applications and reports under the Implementation Doctorate Programme. The form has a strictly defined structure, and each section has a character limit that sets the maximum length of the answer. This limit is intended to enforce concision, clarity and a focus on the most important information, so that applications are both synthetic and rich in content. The application describes the following elements in detail, among others:

Description of the cooperating entity’s experience in cooperation with higher education, science or R&D entities, with particular emphasis on the last 5 years (up to 1,000 characters including spaces)

The “MedTravel” Travel Medicine Centre conducts clinical and diagnostic activity in travel-related disorders, including motion sickness. As part of its activities, the centre provides health services and keeps patients’ medical records, covering the course of the disease, the therapeutic methods used and an assessment of their effectiveness. Over the last five years, the centre has cooperated with research institutions on research into various aspects of human well-being and methods of assessing it. Three research and implementation projects have been carried out in this area, concerning sleep disorders, the diagnosis and treatment of degenerative spinal diseases, and relaxation therapy. The company also takes medical students for summer placements and internships. Specialists employed at MedTravel also give guest lectures at several universities in Poland.

The cooperating entity’s potential for carrying out the project (research equipment, industry experience, etc.) and information on the support offered by the entity employing the doctoral candidate, including a description of the assistant supervisor and their R&D experience (up to 1,500 characters including spaces).

MedTravel has been on the market since 2003. It employs 22 people and also has contracts with several specialists who see patients for 3 to 6 hours a week. The centre has clinical and diagnostic facilities, access to a population of patients with motion sickness and a medical records system that makes it possible to analyse the course of treatment. In terms of infrastructure, it is equipped with systems for diagnosing the vestibular system, including videonystagmography (VNG) systems, caloric tests and devices for assessing the vestibulo-ocular reflex. The centre is also equipped with stations for assessing balance and motor coordination, including stabilometric platforms and posture analysis systems. For therapy, vestibular rehabilitation stations and training programmes aimed at sensory adaptation are available. Another important element of the infrastructure is systems enabling controlled exposure to stimuli that provoke motion sickness symptoms, including motion simulators and virtual reality (VR) stations, which make it possible to conduct research under near-real conditions while precisely controlling stimulus parameters. The assistant supervisor will be Dr Piotr Majchrzak, a participant in implementation-oriented projects who has specialised in the treatment of motion sickness for 10 years. The company offers the doctoral candidate support in implementing the project results.

The aim of the project is to identify the factors affecting the effectiveness of motion sickness therapy and to develop principles of personalised therapeutic management. The research problem arises from the variability of patients’ responses to treatment methods observed in clinical practice. Despite the availability of various forms of therapy, there are no clear guidelines for selecting the optimal treatment method for a specific patient. Motion sickness has a complex pathophysiology, and its course depends on many factors, including individual vestibular sensitivity, psychological factors, the type of stimuli provoking symptoms and the patient’s previous experience. The scientific problem consists in determining which of these factors are crucial for the effectiveness of treatment and whether it is possible to identify groups of patients requiring different therapeutic management.

A maximum of seven (7) expressions separated by commas. Each expression may consist of several words.

Keywords: motion sickness, kinetosis, personalised therapy, vestibular system, treatment effectiveness, travel medicine

Motion sickness (kinetosis) is a disorder resulting from a sensory conflict between information from the vestibular, visual and proprioceptive systems. It occurs most often when travelling by various means of transport, but also in virtual environments (so-called cybersickness). It manifests itself as nausea, dizziness, pallor, excessive sweating and vomiting. It is caused by abnormal processing of motion stimuli in the central nervous system. The state of knowledge indicates that susceptibility to motion sickness varies between individuals and depends, among other things, on age, sex, genetic factors and adaptive experience. Children and women are more sensitive. Treatment uses pharmacotherapy and non-pharmacological methods, but their effectiveness varies. Contemporary research focuses on the mechanisms of vestibular adaptation and the possibilities of individualising therapy.

The project is of significant importance for the development of the medical sciences, in particular neurology, otolaryngology and transport medicine. Understanding the relationships between patient characteristics and the effectiveness of therapy is one of the key issues of contemporary medicine, especially given the growing importance of personalised medicine. The project will help to deepen knowledge of the mechanisms of the body’s response to stimuli causing motion sickness, to identify the factors determining the effectiveness of treatment and to develop an approach that allows therapy to be individualised. The research results will form the basis for new clinical recommendations for the treatment of motion sickness.

The scope for implementing the project results is very wide, and the effect of the project will concern a large part of society. Motion sickness affects 20–40% of the population; as many as one in three people are susceptible to it, and in unfavourable conditions symptoms can appear in most of the population (in extreme situations almost anyone can experience it). The project is of significant practical importance for the activities of the cooperating entity and for the healthcare system. Developing principles of personalised treatment will help to increase the effectiveness of therapy, shorten the time needed to select therapy, limit adverse effects and improve patients’ quality of life. The implementation of the project results will take the form of clinical management schemes, therapeutic recommendations for doctors and tools supporting decision-making in clinical practice.

The project is of significant economic importance given the growing mobility of society and the rapid development of the transport sector and digital technologies. Motion sickness matters, among other things, in industries such as transport, logistics, tourism, aviation and shipping. It also matters for the tourism sector, where more effective treatment can make tourist services accessible to a wider group of customers. As for the development of digital technologies, including virtual reality (VR) systems and simulators, reducing cybersickness will increase the safety and effectiveness of these solutions. From the perspective of the healthcare system, the project can help to optimise treatment costs by shortening the time needed to select effective therapy and limiting the use of ineffective treatment methods. Implementing the project results will also help to optimise treatment costs (shorter time to select therapy) and will lay the foundations for the development of medical services and therapeutic programmes, which will have a positive effect on the development and innovativeness of the medical sector.

The project will be carried out in several stages. Stage 1 is an analysis of the state of knowledge and preparation of the study: a literature review, development of the study protocol and obtaining bioethics committee approval. The milestone is an approved study protocol. Stage 2 is the collection and preparation of clinical data: patient recruitment, collection of data on symptoms and therapy, and verification of data quality. The milestone is a database of patients and their complaints. Stage 3 is an analysis of the factors affecting the effectiveness of therapy: analysis of the relationships between patient characteristics and treatment outcomes, and identification of key clinical factors. The milestone is a set of factors affecting the effectiveness of therapy. Stage 4 is the development of principles for personalising treatment: identifying groups of patients, developing therapeutic schemes and verifying them in clinical practice (testing the recommendations developed and assessing their effectiveness). The milestone is a ready clinical management model, confirmed in operation and ready for full implementation.

The risk analysis identified several areas. Scientific risks are an insufficient number of patients, a lack of significant clinical relationships and high variability of response to treatment. The mitigation measures adopted are increasing the number of centres collecting data and extending the analysis criteria. Organisational risks are difficulties in recruiting patients and delays in the research. The mitigation measures assumed are staged recruitment and progress monitoring. Ethical and legal risks are delays in obtaining bioethics approval and restrictions on access to medical data. The mitigation measures defined are proper preparation of documentation and full anonymisation of data.

Scientific aspects (including publication plans) and aspects related to preparing the implementation must be clearly separated. Each semester should include both research activity and activity preparing for implementation. The version of the schedule in the form of the individual research plan (IRP), valid for the duration of the project, will be submitted in the report after the first year of the doctorate.

Publication of the results in three journals is also planned, after the second, fourth and seventh semesters respectively. The publications will concern methods of collecting and analysing medical data and the treatment of motion sickness.

Description of the scientific or artistic competences of the academic supervisor in the subject area of the project, with a description of the supervisor’s scientific or artistic achievements in this area, with particular emphasis on the last 5 years (the description should include the most important publications and/or artistic works on the subject of the project) (up to 1,000 characters including spaces)

Professor Joanna Szymańska works at the Department of Neurology and Otolaryngology, Faculty of Medicine, Medical University. Her research focuses on the functioning of the vestibular system, balance disorders and the mechanisms of motion sickness. Her record includes more than 170 publications in indexed journals, concerning among other things the pathophysiology of vestibular disorders, sensory adaptation, personalised medicine and the effectiveness of various therapeutic methods used in the treatment of dizziness and kinetosis. The results of her research have been presented at numerous national and international conferences in neurology, otolaryngology and transport medicine. In total she has more than 2,000 citations and an h-index of 32 (Scopus). She has a significant record of reviewing scientific papers and research projects. She has taken part in research projects and has cooperated with clinical centres on research into balance disorders.

Description of the academic supervisor’s experience in cooperation with the socio-economic environment, with particular emphasis on the subject of the project and the last 10 years (the description should include experience in supervising work, including doctoral theses, design, engineering, technological, implementation or artistic work, and R&D projects) (up to 1,000 characters including spaces).

Prof. Szymańska has supervised 9 doctoral theses. She has many years of experience in research projects carried out in cooperation with healthcare entities and the socio-economic environment. She has led more than a dozen projects, including: Assessment of the effectiveness of vestibular rehabilitation in patients with chronic dizziness, Use of virtual reality in the treatment of vestibular disorders, and a Programme for the prevention of balance disorders in occupational groups exposed to motion stimuli. She cooperates with healthcare providers, medical technology companies (development of therapeutic and diagnostic tools) and transport-related institutions. As part of this cooperation, clinical studies have been carried out and new therapeutic methods implemented. She is involved in educational activities, including training for medical staff.

Information on adapting the doctoral school’s education programme to the assumptions of the Implementation Doctorate 2024 Programme (methods of working with the doctoral candidate, the possibility of combining the education programme with professional activity, alignment of the education schedule with the project tasks planned, the possibility of an individual study path, tutoring, crediting learning outcomes through exams/summer schools/certificates, choice of courses, reduced professional placement requirements, hybrid learning, etc.) (up to 500 characters including spaces)

The education programme comprises 600 hours over four years, which makes it easy to conduct research as part of an implementation doctorate. Classes at the Doctoral School are held in the afternoon, so they can be combined with professional work; remote participation is also possible. Professional placements last 50 hours and take place throughout the period of study.

Information on the support offered to the doctoral candidate by the applicant, including the use of resources for the project (infrastructure, materials, methodological assistance, funding) (up to 500 characters including spaces)

The Medical University has modern research infrastructure, including specialist equipment for biomedical analyses and the development of personalised therapies. It provides comprehensive substantive, financial, IT and library support. Experts in various fields will support the doctoral candidate in her research. The institution’s extensive experience is confirmed by numerous national and international projects, including projects on innovative therapeutic solutions.

The application prepared in this way is evaluated by the advisory team for the Implementation Doctorate Programme. The individual evaluation criteria are discussed below; the precondition for evaluation is the implementation character of the project. If this criterion is not met, the application is rejected and not evaluated further. The reviewer therefore assesses:

Does the project have a clearly defined implementation character? (Yes/No)

After the research stage, the project envisages implementing the solution developed in the area of personalised therapy, using artificial intelligence algorithms. The system will support specialists in making therapeutic decisions, among other things by analysing patient data and selecting optimal treatment methods. Given the growing importance of personalised medicine and its use in many areas of healthcare, the implementation potential of the project results is very wide and covers various areas of clinical practice.

Criterion 1.

Scientific or artistic achievements of the supervisor in the subject area of the project (15 / 15)

Justification: The doctorate is being carried out in the biomedical sciences and biomedical engineering, with particular emphasis on the development of personalised therapeutic strategies. The academic supervisor is a recognised expert in modern methods of medical data analysis, modelling of biological processes and applications of artificial intelligence in medicine. Her record includes numerous scientific publications and presentations at national and international conferences. She also reviews scientific papers and is active in expert bodies, which confirms her established position in the scientific community.

Criterion 2.

The supervisor’s achievements in cooperation with the socio-economic environment (10 / 10)

Justification: The academic supervisor actively cooperates with the socio-economic environment, particularly in implementing innovative solutions in healthcare. She has taken part in numerous research and implementation projects, including those related to the development of modern therapeutic methods and their implementation in practice. She has also served as supervisor and reviewer of doctoral theses, which confirms her competence in training research staff.

Criterion 3.

Scientific or artistic significance of the project (15 / 15)

Justification: The project concerns the rapidly developing area of personalised medicine, in which precise analysis of patient data and individualisation of therapy are crucial. The characterisation of biological processes and the body’s response to treatment is an important element from both a clinical and an implementation perspective. Solutions based on artificial intelligence, which are also the subject of intensive research, are playing an increasingly important role here. All these aspects have been taken into account in the project, which determines its high scientific value. The planned research will contribute to the development of the discipline and lay the foundations for wider use of interdisciplinary tools in personalised therapy.

Criterion 4.

Definition of the scientific problem and the implementation project, methodology of the project (20 / 20)

Justification: The scientific problem has been formulated correctly and concerns the development of personalised therapy, with particular emphasis on data analysis at the molecular and cellular level. The research methodology takes a comprehensive approach — from a literature review, through the development of research methods and data preparation, to the creation and implementation of a ready-made solution. The implementation stage provides for the development of software supporting specialists in planning and optimising therapy which — thanks to the use of artificial intelligence algorithms — will support clinical decision-making and increase the effectiveness of treatment.

Criterion 5.

Scope of support for the doctoral candidate from the applicant and the cooperating entity — the future doctoral candidate’s employer (10 / 10)

Justification: The applicant has modern research infrastructure, including advanced laboratories equipped with specialist apparatus for biomedical research, and has declared that it will be used for the doctorate. It also provides substantive support from experts in various fields and appropriate funding for the project. The cooperating entity also offers access to well-equipped laboratories and modern technological infrastructure, which significantly strengthens the research and implementation potential of the undertaking.

Criterion 6.

The applicant’s experience in implementation activity, with particular emphasis on previous participation in the Implementation Doctorate Programme (5 / 5)

Justification: The Medical University already has experience of taking part in the programme. Cooperation so far has been positive, and all the university’s obligations have been fulfilled on time. The reports submitted by the university have also been of a high formal and substantive standard.

Criterion 7.

Impact of implementing the project on selected sectors of the economy or on society, including the significance of the implementation for the cooperating entity (15 / 15)

Justification: Implementing the project results will have a significant impact on the healthcare system and on society. More effective treatment of motion sickness will improve patients’ quality of life and mobility, which matters in the context of growing professional and social activity. In the healthcare sector, the project will improve the effectiveness of treatment by introducing more structured principles of therapeutic management, which will help to reduce treatment costs and the number of ineffective therapies. The project is also relevant to the transport, tourism and simulation training sectors, where reducing motion sickness symptoms will improve travel comfort and the effectiveness of simulation technologies. From the cooperating entity’s perspective, implementing the project will enable the development of modern, personalised medical services, increasing its competitiveness and attractiveness to patients.

Criterion 8.

The cooperating entity’s experience in cooperation with higher education and science entities and the R&D sector (10 / 10)

Justification: The cooperating entity, MedTravel, has experience of cooperating with universities, with which it has carried out 3 research and implementation projects. The centre is also involved in training future medical staff, offering medical students placements and internships. In addition, specialists working at MedTravel are active in teaching, giving classes and lectures at selected universities in Poland.

Summary of the evaluation

Total (100 / 100)

Recommendation: Positive

Recommended for funding: Yes

Justification: The project has been prepared coherently and concerns an important and topical clinical problem of both scientific and practical significance. The research concept presented is in line with the development of personalised medicine and responds to real needs related to improving the effectiveness of motion sickness treatment. The academic supervisor has an appropriate research record and experience in conducting research and cooperating with healthcare entities, which provides a solid substantive basis for the project. The research institution has adequate infrastructure and staff, providing conditions conducive to high-quality research. The cooperating entity offers access to clinical facilities and an environment enabling the results to be verified and implemented in practice. The project as a whole has high implementation potential and a real chance of achieving the planned results.

Name: Marta Stachowiak

University: Medical University

Faculty: Medicine

Supervisor (academic supervisor): Joanna Szymańska

Auxiliary supervisor: not applicable

Additional information: Assistant supervisor — Piotr Majchrzak, cooperating entity — “MedTravel” Travel Medicine Centre

Discipline: Medical sciences

a) thesis title

Personalised therapy of motion sickness

b) research hypothesis

The varying effectiveness of the therapeutic methods used to treat motion sickness results from the complex pathophysiology of the disorder and the high inter-individual variability of patients. The choice of appropriate therapeutic management in clinical practice often still depends on the doctor’s experience, and its effectiveness is determined by many factors, such as vestibular sensitivity, the nature of the stimuli provoking symptoms and psychological factors. At the same time, the development of knowledge about vestibular disorders and therapeutic methods makes it possible to better understand the mechanisms of response to treatment and to identify the factors that determine its effectiveness. It is therefore possible to develop a personalised therapeutic approach to motion sickness, supporting the selection of the optimal treatment method for a specific patient.

c) rationale for the choice of topic, research aim and methodology

The complex nature of motion sickness and the varied response of patients to the therapeutic methods used make it difficult to choose the optimal clinical management. In practice, this means that the effectiveness of treatment depends largely on the doctor’s experience, and the effect of therapy itself is determined by many interrelated factors, such as vestibular sensitivity, the nature of the stimuli provoking symptoms, psychological factors and the patient’s previous experience. At the same time, the development of knowledge about the pathophysiology of vestibular disorders and therapeutic methods makes it possible to determine more precisely the factors affecting the effectiveness of treatment. In particular, it seems reasonable to develop an approach that makes it possible to select therapy tailored to the individual patient’s characteristics, taking into account both pharmacological and non-pharmacological methods.

The aim of the planned research is a detailed identification of the factors determining the effectiveness of motion sickness therapy, the development of principles of personalised therapeutic management and an assessment of the possibility of implementing them in clinical practice. These goals will be achieved in stages. In the first stage, a literature review will be carried out and a study protocol developed, covering the selection of clinical variables and methods for assessing the effectiveness of therapy. Next, patients will be recruited and data collected on the course of the disease, the characteristics of symptoms, triggering factors and the treatment methods used. The data collected will be analysed to identify relationships between patient characteristics and the effectiveness of therapy and to identify the key factors affecting the response to treatment. On this basis, using clinical knowledge and current guidelines, principles for selecting therapy for selected groups of patients will be developed.

The next stage will be the verification of the therapeutic schemes developed under clinical conditions, by applying them in practice and assessing their effectiveness compared with standard management. An important element of the work will also be the development of recommendations enabling the research results to be used in doctors’ everyday work. The work will culminate in an analysis of the effectiveness of the approach developed and its implementation in the activities of the cooperating entity, which will make it possible to use personalised methods of treating motion sickness under real conditions.

d) expected results and their significance and impact for science and society

The research will make it possible to achieve the scientific goals of the project, in particular those related to identifying the factors affecting the effectiveness of motion sickness therapy and developing principles of personalised therapeutic management. In the course of the work, a set of clinical data will be collected, covering patient characteristics, the course of the disease, factors triggering symptoms and the results of the treatment methods used, together with an assessment of their effectiveness in different groups of patients. These data will form the basis for building a structured knowledge base enabling the selection of an appropriate therapeutic strategy depending on the patient’s individual characteristics. One of the key results of the project will be the development of clinical management schemes that take into account the diversity of patients and the specific course of the disease. The approach developed will allow a more informed and structured choice of therapy, both pharmacological and non-pharmacological. Another important element will be the preparation of recommendations enabling the research results to be used in doctors’ everyday work and implemented in the activities of the cooperating entity. This will make it possible to increase the effectiveness of treatment, shorten the time needed to select the right therapy and reduce the risk of using ineffective therapeutic methods.

The significance of the proposed research for the development of science lies above all in deepening knowledge of the mechanisms of motion sickness and of the relationships between patients’ clinical characteristics and their response to treatment. The database and the results of the analyses can form the basis for further research on personalising therapy in vestibular disorders and on the development of individualised approaches in medicine. The project thus fits in with current directions in the development of medicine focused on the individual needs of the patient. The project’s impact on the economy and society will be multidimensional. Motion sickness affects the functioning of many sectors, in particular transport, tourism and areas using simulation technologies. Developing more effective treatment methods will help to improve patients’ quality of life and increase their professional and social activity. Implementing the principles of personalised therapy can also improve the efficiency of the healthcare system through more rational use of the available treatment methods. In the context of growing mobility and the development of new technologies such as virtual reality, solutions of this kind are important support both for clinical practice and for the development of modern medical services. As a result of the project, it will be possible to create a therapeutic approach that not only meets current clinical needs but also sets the direction for the further development of motion sickness treatment towards greater personalisation and effectiveness.

Publication of the results in three journals is also planned, after the second, fourth and seventh semesters respectively. The publications will concern methods of collecting and analysing medical data and the treatment of motion sickness.

The project provides for the publication of three scientific articles in scored journals on the MNiSW list (at least 70 points), after the second, fourth and seventh semesters respectively. Participation in two scientific conferences with posters or papers is also planned.

The doctoral thesis is planned to be submitted in September 2028.

Not applicable

The programme provides for annual reporting, so each doctoral candidate is required to prepare a report on their work after each successive year of the project. The report is evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

The first, second and third years of the doctoral candidate’s work end with a set of documents comprising the report and the supervisors’ opinions, and an evaluation of this material. After the fourth year, a final report is prepared — also presented at the end of the model prepared for mechanical engineering. Here, the declaration that the doctoral thesis has been submitted is particularly important.

The doctoral thesis was submitted by the required deadline. A scan of the first page of the thesis is shown below.

The final report is the last report prepared by the doctoral candidate and submitted together with the supervisors’ opinions. It covers the entire project (all years), not just the last year. This report is also evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

This set of reports and the evaluation close the doctoral candidate’s entire period of activity. It also marks the end of the work of the expert from the advisory team for the Implementation Doctorate Programme on this particular project. The team does not evaluate doctoral theses; the appointment of reviewers and the rest of the procedure are the responsibility of the relevant Discipline Councils.

A very important area of contemporary social sciences is the energy transition and social adaptation to new technologies. The transformation processes currently taking place in the global economy include not only technological and industrial changes but also profound social, cultural and economic changes affecting the functioning of contemporary societies. The growing importance of climate policy, the need to reduce greenhouse gas emissions and the drive to increase energy security mean that states and international organisations are intensifying efforts to develop modern energy sources and low-emission technologies. The energy transition is thus becoming not only a technological and economic challenge but also a process requiring the active participation of society, the building of new civic competences and the shaping of attitudes conducive to accepting change. In recent years, hydrogen technologies have become particularly important; they are seen as one of the key elements of the transition of the economy towards climate neutrality and of increasing the energy security of countries and regions. Hydrogen is increasingly regarded as a strategic energy carrier of the future that can be used in power generation, industry, transport and energy storage. Looking further ahead, its use in nuclear fusion is also becoming increasingly apparent. The development of the hydrogen economy is also part of climate policies pursued at national and international level, in line with the European Green Deal, climate neutrality strategies and sustainable development efforts.

The development of hydrogen technologies covers not only technical and industrial aspects but also social, educational and communication issues. Implementing new energy technologies requires social acceptance, an understanding of their importance and the building of citizens’ competences in the informed use of modern energy solutions. In practice, this means conducting educational, information and social activities aimed at residents, companies, public administration and the education sector. A particular issue at present is the level of public knowledge about hydrogen as an energy carrier. There are numerous barriers in society related to a lack of knowledge, stereotypes about the safety of hydrogen technologies and an insufficient understanding of their importance for the economy and the environment. At the same time, the rapid development of digital technologies and social media creates new opportunities for social communication, education and shaping civic attitudes. The development of tools based on artificial intelligence also makes it possible to analyse social attitudes, identify sources of disinformation and design more effective models of social communication supporting the energy transition.

The contemporary energy transition requires not only the development of technological infrastructure but also building society’s readiness to take part in economic, environmental and technological change. This readiness includes citizens’ level of knowledge, acceptance of new technologies, the ability to adapt to change and social resilience to disinformation and messages questioning the legitimacy of the energy transition. In this context, activities aimed at building an informed society using hydrogen technologies, capable of active and responsible participation in the development of a low-emission economy, are becoming particularly important.

Below is a model example of a solution based on cooperation between an industry organisation and a university under the Implementation Doctorate Programme. For the purposes of the model, an illustrative project carried out jointly by an organisation from the hydrogen economy sector and a research institution working in the social sciences has been assumed.

Polska Izba Wodoru (the Polish Hydrogen Chamber) is an industry organisation bringing together about 130 businesses working in hydrogen technologies, the energy transition and modern low-emission technologies. The Chamber operates under the Act on Chambers of Commerce and its Statute and has legal personality. It operates in the Republic of Poland and also abroad; it may undertake and take part in business ventures and conduct business activity. Its members are industrial companies, technology firms, energy sector entities, R&D institutions and organisations supporting the development of the hydrogen economy in Poland. The Chamber’s activities focus on supporting the development of hydrogen technologies, integrating the business and academic communities, conducting educational activities and popularising knowledge about the energy transition.

In recent years, the organisation has observed the growing importance of the social aspects of the energy transition. Despite the rapid development of hydrogen technologies and a growing number of infrastructure and investment projects, a noticeable problem remains the low level of public awareness of hydrogen as an energy carrier and a limited understanding of the role of the hydrogen economy in the energy transition. There are also numerous stereotypes and fears in the public sphere about the safety of hydrogen technologies, as well as disinformation affecting the level of social acceptance of new energy technologies. Polska Izba Wodoru conducts information, educational and popularisation activities aimed at companies, public administration, the academic community and society at large. It organises conferences, training courses, social campaigns and events promoting the development of the hydrogen economy. As part of these activities, data are collected on the level of interest in hydrogen, social attitudes towards new energy technologies and the effectiveness of individual forms of social communication. At the same time, the rapid development of digital technologies, social media and tools based on artificial intelligence creates new opportunities for analysing social attitudes, monitoring public opinion and designing more effective models of social communication. The organisation sees the need to develop tools for diagnosing the level of social readiness for the energy transition and identifying the factors affecting the acceptance of hydrogen technologies. The expected result of the research would be a model for analysing social readiness for the energy transition, including mechanisms for diagnosing the level of public knowledge, analysing the acceptance of hydrogen technologies, identifying disinformation and recommending educational and communication activities. Polska Izba Wodoru also hopes to develop the organisation’s competences in using modern analytical tools and artificial intelligence to support social communication and energy education.

Given the interdisciplinary nature of the issue, covering elements of sociology, social communication, the energy transition and data analysis, Polska Izba Wodoru decided to cooperate with a university with experience in social research and projects analysing transformation processes. This cooperation is intended to enable a research and implementation project under the Implementation Doctorate Programme, which will result in the development and implementation of tools supporting the building of an informed society prepared to take an active part in the energy transition. The University of Social Sciences and Technology was therefore chosen, as it works on social analysis and has extensive research experience in this area. It employs Professor Robert Andrzejewski, who specialises in the sociology of technological transformation, social communication and the social aspects of the development of modern energy technologies. His research focuses on processes of social adaptation to technological change, building public awareness of the energy transition and analysing the influence of digital media and artificial intelligence on civic attitudes. In recent years his research interests have also included social readiness for the energy transition and the role of social and business organisations in building civic awareness of the low-emission economy and hydrogen technologies. Professor Robert Andrzejewski is the author of numerous publications on the sociology of new technologies, social communication and the digital aspects of social transformation. He has also taken part in research and implementation projects carried out in cooperation with industry organisations, local government units and energy sector entities. He will be supported by Dr Joanna Małecka, who works in the same department and specialises in analytical methods used in social communication. The cooperating entity proposed Jerzy Zaremba, MA. He has many years of experience in activities related to the development of energy technologies, cross-sector cooperation and projects supporting the energy transition and the development of the hydrogen economy. As part of his work at Polska Izba Wodoru, he helps to coordinate initiatives integrating the academic, business and administrative communities in hydrogen technologies and modern energy solutions. His competences include cooperation with energy and technology companies and the coordination of educational and social projects aimed at implementing activities to popularise the hydrogen economy, which fits the subject of the project perfectly. On behalf of the Chamber he has led a number of projects and initiatives related to the development of the hydrogen economy and the energy transition, including popularising hydrogen technologies among local government units, educational initiatives building public awareness of the energy transition, cooperation between science, business and public administration, developing recommendations and strategies for hydrogen technologies, and promoting modern low-emission technologies and sustainable development (e.g. Hydrogen Valley, regional hydrogen strategies, ESG projects, activities related to the European Green Deal). He also organises conferences and business forums on the hydrogen economy, supports the development of regional hydrogen valleys and handles communication on the social aspects of the energy transition. He also takes part in preparing analyses, recommendations and projects supporting the development of the hydrogen economy in Poland. It is worth noting here that an assistant supervisor does not need to hold a doctoral degree, but if they do not, they must have a significant record of implementation activity — implementations, patents — or at least five years of research experience. In the project, Jerzy Zaremba will act as assistant supervisor responsible for implementation support and for providing access to the organisational and expert environment of Polska Izba Wodoru. The doctoral candidate is Wojciech Kozłowski, MA, a graduate of a master’s programme in sociology specialising in social communication and socio-technological transformation processes. During his studies, his research interests centred on the impact of new technologies on society, social communication in the digital environment and the social aspects of the energy transition and sustainable development. In his thesis he analysed the influence of digital media on shaping social attitudes towards technological and environmental change, with particular emphasis on the role of social media in building civic awareness. His research interests also include social communication, building public awareness, social acceptance of technological innovation, social resilience to disinformation, the use of artificial intelligence and data analysis in social research, and the influence of digital media on shaping public opinion. Given the interdisciplinary nature of his interests and his wish to combine social research with practical implementation, Wojciech Kozłowski decided to carry out his project as an implementation doctorate in cooperation with Polska Izba Wodoru, which will employ him if the application setting out the concept of the research and implementation project, submitted under the Implementation Doctorate Programme, is assessed positively.

*usually, at the application stage, the company has already chosen the doctoral candidate, but formally they do not appear in the application. They are added only after the application has been approved for funding by MNiSW and after successful admission to the Doctoral School. Only then are they formally registered in the project.

No conflict of interest was found between the parties involved. The parties agree that the doctoral candidate will have access to research equipment at the university and infrastructure at the company. It was also agreed that intellectual property created as a result of the project will be managed as follows: if it is created on the company’s side, it will belong to the company; if it is created as a result of work at the university, it will belong to the university; if it is created as a result of joint

The concept is then translated into a formal application, prepared on the basis of the form developed by the advisory team to the Minister of Science and Higher Education mentioned earlier, responsible for evaluating applications and reports under the Implementation Doctorate Programme. The form has a strictly defined structure, and each section has a character limit that sets the maximum length of the answer. This limit is intended to enforce concision, clarity and a focus on the most important information, so that applications are both synthetic and rich in content. The application describes the following elements in detail, among others:

Description of the cooperating entity’s experience in cooperation with higher education, science or R&D entities, with particular emphasis on the last 5 years (up to 1,000 characters including spaces)

Polska Izba Wodoru is an industry organisation bringing together about 130 businesses working in hydrogen technologies, the energy transition and modern low-emission technologies. The Chamber works for the development of the hydrogen economy, the integration of the business and academic communities and the popularisation of knowledge about the energy transition. As part of its activities, it organises conferences, training courses, social campaigns and educational activities aimed at companies, public administration and society. In recent years, the Chamber has cooperated with research and business entities on initiatives related to the development of regional hydrogen valleys, energy education and social communication on the energy transition. The organisation also supports the analysis of social attitudes towards new energy technologies.

The cooperating entity’s potential for carrying out the project (research equipment, industry experience, etc.) and information on the support offered by the entity employing the doctoral candidate, including a description of the assistant supervisor and their R&D experience (up to 1,500 characters including spaces).

Polska Izba Wodoru has been operating since 2024. It employs 15 people and cooperates with numerous industry specialists in energy, new technologies and the energy transition. The organisation has experience in activities related to the development of the hydrogen economy, social communication on new energy technologies and cooperation between science, business and public administration. The Chamber brings together entities from the energy, industrial, technology and R&D sectors, which provides access to a broad expert community and practical implementation facilities. It conducts educational, information and analytical activities to popularise hydrogen technologies and organises conferences, training courses and social campaigns. As part of its activities, it collects data on social attitudes towards the energy transition and the effectiveness of communication and educational activities. The entity provides the doctoral candidate with organisational and substantive support, access to analytical materials, industry events and the hydrogen economy expert community. The assistant supervisor will be Jerzy Zaremba, who has experience in projects related to energy education, social communication and the popularisation of low-emission technologies.

The scientific goal of the project is to develop a model for building public awareness supporting the development of hydrogen technologies and for diagnosing social readiness for the energy transition. The research problem concerns identifying the factors affecting the level of public knowledge, the acceptance of hydrogen technologies and society’s resilience to disinformation about the energy transition. The project involves analysing the influence of social communication, civic education and digital media on social attitudes towards the hydrogen economy. Another important element of the research will be an assessment of the possibilities of using artificial intelligence tools to analyse social attitudes and support educational activities. The research hypothesis is that it is possible to develop an integrated model of social communication and education that increases the level of social readiness for the energy transition and acceptance of hydrogen technologies.

A maximum of seven (7) expressions separated by commas. Each expression may consist of several words.

Keywords:

hydrogen technologies, energy transition, public awareness, social communication, social readiness for transition, artificial intelligence, disinformation

The project is of great scientific importance in sociology, as it concerns the social aspects of the energy transition and the processes by which society adapts to new technologies. Research to date has focused mainly on the technical and economic aspects of the development of the hydrogen economy, while issues related to the level of public awareness and the influence of social communication on the acceptance of hydrogen technologies remain insufficiently understood. Knowledge of the influence of digital media, disinformation and artificial intelligence tools on social attitudes towards new energy technologies is particularly limited. The rationale for addressing the research problem is also the growing importance of hydrogen technologies in the energy transition and the need to build social acceptance for a low-emission economy. A significant challenge remains the identification of the factors affecting the level of public knowledge, trust in technology and social resilience to disinformation about the energy transition.

The novelty of the project lies in combining sociological research on public awareness and social communication with the analysis of digital data and the use of artificial intelligence tools to monitor and analyse social attitudes. The project involves developing a model of social readiness for the energy transition and tools supporting the building of public awareness of hydrogen technologies. The results will contribute to the development of knowledge in the sociology of new technologies, social communication and the social aspects of the energy transition. The research results will also form the basis for further analyses of social adaptation to technological change, the influence of digital media on the formation of public opinion and the use of artificial intelligence in social research and social communication.

The project is of practical importance not only for Polska Izba Wodoru but for all entities involved in the energy transition. Its result will be the development and implementation of a system supporting social readiness for the energy transition, comprising a model for analysing public awareness of hydrogen technologies, tools for monitoring social attitudes and recommendations on social communication and energy education. The system will use elements of digital data analysis and artificial intelligence tools to help identify social attitudes, the level of acceptance of hydrogen technologies and disinformation related to the energy transition. The place of implementation will be Polska Izba Wodoru, which will use the solutions developed in educational, information and analytical activities carried out in cooperation with energy companies, local government units and the academic community. The model developed will support the planning of social campaigns, educational activities and initiatives to popularise the hydrogen economy and build social acceptance for the energy transition. The significance of the project for the cooperating entity lies in the possibility of developing the organisation’s competences in using modern methods of social data analysis, social communication and artificial intelligence.

The project is also of significant importance for the Polish economy, as it can support the development of social conditions for the energy transition and increase social acceptance of low-emission technologies. The research results can be used by companies, public administration and industry organisations in designing activities related to the energy transition, social education and countering disinformation about new energy technologies.

The project will be carried out in stages. Stage 1 covers an analysis of the state of knowledge on the social aspects of the energy transition, hydrogen technologies, social communication and the use of artificial intelligence in social research. A literature review, an analysis of social communication models and the development of the research methodology will be carried out. Milestone — preparation of the research model and research tools.

Stage 2 covers the collection and preparation of social data. Surveys, interviews and an analysis of data from digital and social media will be carried out. The aim of this stage will be to identify the level of public awareness of hydrogen technologies, attitudes towards the energy transition and the factors affecting the acceptance of new energy technologies. Milestone — a database of social readiness for the energy transition.

Stage 3 covers an analysis of the factors affecting social readiness for the energy transition. The relationships between the level of public knowledge, social communication, disinformation and attitudes towards hydrogen technologies will be analysed. Artificial intelligence tools will also be used to analyse digital data and monitor social attitudes. Milestone — a set of factors affecting the level of social acceptance of hydrogen technologies.

Stage 4 covers the development and verification of a system supporting social readiness for the energy transition. Recommendations on social communication, energy education and countering disinformation will be prepared. The model developed will be verified in the activities of Polska Izba Wodoru. Milestone — a ready model of social communication and education prepared for implementation.

The risk analysis identified scientific risks — insufficient social data and difficulties in identifying relationships between the factors analysed. Mitigation — extending the research groups and using different data analysis methods. Organisational risks — delays in carrying out social research and collecting data. Mitigation — staged research and monitoring of project progress. Technological risks — limitations related to digital data analysis and the use of AI tools. Mitigation — cooperation with data analysis specialists and the use of verified analytical methods.

Scientific aspects (including publication plans) and aspects related to preparing the implementation must be clearly separated. Each semester should include both research activity and activity preparing for implementation. The version of the schedule in the form of the individual research plan (IRP), valid for the duration of the project, will be submitted in the report after the first year of the doctorate.

Three scientific publications are also planned: after the second semester on the social aspects of the energy transition and hydrogen technologies, after the fourth on social communication and the analysis of attitudes towards new energy technologies, and after the sixth on the use of artificial intelligence in analysing social readiness for the energy transition.

Description of the scientific or artistic competences of the academic supervisor in the subject area of the project, with particular emphasis on the last 5 years (the description should include the most important publications and/or artistic works on the subject of the project) (up to 1,000 characters including spaces)

Professor Robert Andrzejewski works at the Department of Sociology and Social Communication, Faculty of Social Sciences, University of Social Sciences and Technology. His research focuses on the social aspects of technological transformation, social communication and the processes by which society adapts to changes related to the development of new technologies. His particular interests are the social determinants of the energy transition, the acceptance of technological innovation, resilience to disinformation and the use of artificial intelligence in social research. His record includes more than 140 publications on the sociology of new technologies and social communication. He has more than 1,500 citations and an h-index of 24. He has taken part in numerous research and implementation projects carried out in cooperation with public administration, industry organisations and companies. He has extensive experience in reviewing scientific papers and training young researchers.

Description of the academic supervisor’s experience in cooperation with the socio-economic environment, with particular emphasis on the subject of the project and the last 10 years (the description should include experience in supervising work, including doctoral theses, design, engineering, technological, implementation or artistic work, and R&D projects) (up to 1,000 characters including spaces).

Prof. Robert Andrzejewski has supervised 6 doctoral theses. He has many years of experience in research and research-and-implementation projects carried out in cooperation with public administration, industry organisations and new technology companies. He has led or taken part in projects including those on the social aspects of the energy transition, the development of society’s digital competences, countering disinformation and the use of artificial intelligence in analysing social processes. He cooperates with local government units, non-governmental organisations, businesses and research institutions. The projects have produced models of social communication, educational strategies and tools supporting transformation processes. He is also involved in expert and training activities related to social communication, the energy transition and the development of the information society.

Information on adapting the doctoral school’s education programme to the assumptions of the Implementation Doctorate Programme (methods of working with the doctoral candidate, the possibility of combining the education programme with professional activity, alignment of the education schedule with the project tasks planned, the possibility of an individual study path, tutoring, crediting learning outcomes through exams/summer schools/certificates, choice of courses, reduced professional placement requirements, hybrid learning, etc.) (up to 500 characters including spaces)

The DS programme comprises 600 hours (including electives), which allows research to be carried out freely. Classes are held on Friday afternoons and Saturdays, so they can be combined with professional work (remote option). An individual study path and credit for summer schools are possible. The DS also provides tutoring. Professional placements last 50 hours and can be reduced on the basis of certificates.

Information on the support offered to the doctoral candidate by the applicant, including the use of resources for the project (infrastructure, materials, methodological assistance, funding) (up to 500 characters including spaces)

The university has modern research infrastructure supporting social research, including software for statistical and qualitative analysis and access to scientific databases and digital resources. It provides the doctoral candidate with substantive, methodological, organisational and IT support. Experts in sociology, social communication and data analysis will support the project, drawing on the university’s experience from numerous national and international projects.

The application prepared in this way is evaluated by the advisory team for the Implementation Doctorate Programme. The individual evaluation criteria are discussed below; the precondition for evaluation is the implementation character of the project. If this criterion is not met, the application is rejected and not evaluated further. The reviewer therefore assesses:

Does the project have a clearly defined implementation character? (Yes/No)

After the research stage, the project envisages implementing the “System supporting social readiness for the energy transition” at Polska Izba Wodoru. The system will include a model for diagnosing the level of public awareness of hydrogen technologies, tools for monitoring social attitudes and recommendations supporting educational and communication activities. The solution will use social data analysis methods, including artificial intelligence tools for identifying social attitudes, sentiment analysis and recognising disinformation related to the energy transition. The system will also support entities cooperating with Polska Izba Wodoru in planning educational campaigns, information activities and initiatives to build social acceptance for hydrogen technologies. Given the growing importance of the energy transition and the need to increase public awareness of new energy technologies, the implementation potential of the project results is wide and covers industry organisations, energy companies, local government units and public institutions responsible for energy and climate policies.

Criterion 1.

Scientific or artistic achievements of the supervisor in the subject area of the project (15 / 15)

Justification: The doctorate is being carried out in the discipline of sociology and concerns the social aspects of the energy transition, building public awareness and the acceptance of hydrogen technologies. The academic supervisor, Prof. Robert Andrzejewski, is a recognised specialist in the sociology of new technologies, social communication and socio-technological transformation processes. His research interests include social adaptation to technological change, social communication, resilience to disinformation and the use of modern data analysis methods in social research. The supervisor’s record includes numerous scientific publications and presentations at national and international conferences on the energy transition, the information society and social communication. The professor is actively involved in research and implementation projects and reviews scientific papers and research projects, which confirms his established position in the scientific community.

Criterion 2.

The supervisor’s achievements in cooperation with the socio-economic environment (10 / 10)

Justification: The academic supervisor actively cooperates with the socio-economic environment, particularly in the energy transition, social communication and the social aspects of implementing new technologies. He has taken part in numerous research and implementation projects carried out in cooperation with public administration, industry organisations, local government units and new technology companies. These projects concerned, among other things, the social acceptance of technological innovation, the development of digital competences, countering disinformation and social communication in transformation processes. He has also served as supervisor and reviewer of doctoral theses and contributed to expert opinions and recommendations for public institutions and business organisations, which confirms his experience in cooperating with the socio-economic environment and in training research staff.

Criterion 3.

Scientific or artistic significance of the project (15 / 15)

Justification: The project concerns research on the social aspects of the energy transition, in which public awareness, acceptance of new technologies and society’s readiness to take part in transformation processes are crucial. Hydrogen technologies, one of the pillars of the low-emission economy and climate policy, are becoming particularly important. Despite intensive technological development, the social determinants of implementing hydrogen technologies, the influence of social communication on their acceptance and the impact of disinformation on citizens’ attitudes remain insufficiently understood. The novelty of the project lies in combining classic sociological research methods with digital data analysis and the use of artificial intelligence tools to monitor and interpret social attitudes. The project fits in with current research directions on the energy transition, the information society and the use of AI in the social sciences. The planned research will contribute to knowledge in the sociology of new technologies, social communication and social readiness for the energy transition, and its results may form the basis for further interdisciplinary research and practical activities supporting the implementation of innovative energy technologies.

Criterion 4.

Definition of the scientific problem and the implementation project, methodology of the project (20 / 20)

Justification: The scientific problem has been formulated correctly and concerns the identification of the factors affecting the level of public awareness, the acceptance of hydrogen technologies and social readiness for the energy transition. The project takes into account current challenges related to implementing new energy technologies, including the influence of social communication, digital media and disinformation on social attitudes. At the same time, the implementation goal is clearly defined: developing and implementing the “System supporting social readiness for the energy transition” at Polska Izba Wodoru. The research methodology takes a comprehensive approach, from a literature review and the development of research tools, through surveys, interviews and the analysis of digital media data, to the development and validation of the implementation model. The project provides for the use of both classic social research methods and modern data analysis tools, including solutions based on artificial intelligence. In the implementation stage, a system will be developed to support the diagnosis of the level of public awareness, the monitoring of attitudes towards hydrogen technologies and the planning of educational and communication activities. The proposed methodology is coherent, appropriate to the goals set and offers a real chance of achieving both research and implementation results.

Criterion 5.

Scope of support for the doctoral candidate from the applicant and the cooperating entity — the future doctoral candidate’s employer (10 / 10)

Justification: The applicant has adequate research infrastructure supporting social research, including specialist software for statistical and qualitative analysis and access to the scientific databases and digital resources needed for research in the social sciences. It has also declared substantive support from experts in sociology, social communication and data analysis, as well as organisational conditions enabling the project to be carried out. The cooperating entity — Polska Izba Wodoru — offers the doctoral candidate access to a broad expert community of representatives of the energy, industrial, technology and R&D sectors. It also provides access to analytical materials, data on social attitudes, industry events and contacts with hydrogen economy stakeholders. The Chamber’s organisational and substantive support, combined with the applicant’s scientific potential, creates very good conditions for carrying out both the research and the implementation parts of the project.

Criterion 6.

The applicant’s experience in implementation activity, with particular emphasis on previous participation in the Implementation Doctorate Programme (5 / 5)

Justification: The University of Social Sciences and Technology has experience of taking part in the programme. Cooperation so far has been positive, and all the university’s obligations have been fulfilled on time. The reports submitted by the university have also been of an appropriately high formal and substantive standard.

Criterion 7.

Impact of implementing the project on selected sectors of the economy or on society, including the significance of the implementation for the cooperating entity (15 / 15)

Justification: Implementing the project results will have a significant impact on the energy transition and the development of the hydrogen economy in Poland. The “System supporting social readiness for the energy transition” will enable a more effective diagnosis of the level of public awareness, the identification of barriers to the acceptance of new technologies and the planning of educational and communication activities supporting the development of hydrogen technologies. The project will help to increase society’s knowledge of the energy transition, limit the impact of disinformation and increase social acceptance of low-emission solutions. The significance of the project goes beyond the hydrogen economy sector and may also cover public administration, the energy sector, local government units and organisations responsible for climate and energy policies. The research results can support the design of effective communication strategies and educational activities related to the energy transition and the country’s energy security. From the perspective of Polska Izba Wodoru, implementing the project will enable the development of modern analytical tools supporting the organisation’s educational and expert activities. It will also make information activities and cooperation with companies, public administration and the academic community more effective, strengthening the Chamber’s position as an important participant in the energy transition in Poland.

Criterion 8.

The cooperating entity’s experience in cooperation with higher education and science entities and the R&D sector (10 / 10)

Justification: The cooperating entity, Polska Izba Wodoru, has experience of cooperating with universities, research institutions and R&D entities. As part of its activities, the Chamber cooperates with Polish and foreign research centres, supporting initiatives related to the development of hydrogen technologies, the energy transition and social education. The organisation takes part in undertakings integrating science, business and public administration, including conferences, expert projects, educational activities and initiatives related to the development of regional hydrogen valleys. Polska Izba Wodoru is also involved in disseminating research results and transferring knowledge into business practice. Representatives of the Chamber actively take part in scientific and industry events, cooperating with universities and research institutions on activities related to the hydrogen economy, the energy transition and the development of innovative technologies.

Summary of the evaluation

Total (100 / 100)

Recommendation: Positive

Recommended for funding: Yes

Justification: The project is well prepared and concerns a topical and important social problem related to the energy transition and the development of hydrogen technologies. The research concept presented is in line with strategic directions for the development of the low-emission economy and responds to the growing need to build social readiness for technological and energy change. The project combines a classic sociological approach with modern data analysis methods and the use of artificial intelligence tools, which gives it an interdisciplinary and innovative character. The academic supervisor has an appropriate research record and experience in research and implementation projects related to social communication, technological transformation and the analysis of social processes. The research institution has adequate organisational, methodological and staff potential, providing conditions for high-quality research. The cooperating entity — Polska Izba Wodoru — provides access to an expert community, a wide network of stakeholders and practical conditions enabling the project results to be verified and implemented. The system developed for supporting social readiness for the energy transition has high implementation potential and can be used by industry organisations as well as energy companies, public administration and local government units. The project as a whole has high scientific and practical value and a real chance of achieving the planned research and implementation results.

Name: Wojciech Kozłowski

University: University of Social Sciences and Technology

Faculty: Social Sciences

Department: Sociology and Social Communication

Supervisor (academic supervisor): Prof. Robert Andrzejewski

Auxiliary supervisor: Dr Joanna Małecka

Additional information: Assistant supervisor — Jerzy Zaremba, cooperating entity — Polska Izba Wodoru

Discipline: Sociology

a) thesis title

A model for building public awareness supporting the development of hydrogen technologies

b) research hypothesis

The level of social acceptance of hydrogen technologies and readiness to take part in the energy transition varies and results from the complex interaction of social, educational, communication and technological factors. The shaping of social attitudes towards new energy technologies remains largely dependent on citizens’ level of knowledge, the quality of social communication, trust in public institutions and the influence of digital media and disinformation. At the same time, progress in hydrogen technologies and the growing importance of the energy transition create a need to better understand the mechanisms affecting social readiness for technological and energy change. In parallel, the development of social data analysis methods and artificial intelligence tools makes it possible to identify the factors determining the level of public awareness, acceptance of new technologies and resilience to disinformation. It is therefore possible to develop a model for building public awareness supporting the development of hydrogen technologies and a system supporting social readiness for the energy transition, enabling more effective planning of educational, information and communication activities.

c) rationale for the choice of topic, research aim and methodology

The energy transition is one of the most important social, economic and technological challenges of the 21st century. In the face of climate change, the need to reduce greenhouse gas emissions and the need to increase the energy security of countries and regions, the development of technologies enabling a low-emission economy is becoming particularly important. Among them, hydrogen technologies play an important role and are seen as one of the key elements supporting the decarbonisation of industry, energy and transport. The rapid development of the hydrogen economy means that not only technical and economic issues but also the social determinants of implementing new technologies are becoming increasingly important. Experience with implementing technological innovations shows that the success of the energy transition depends not only on the availability of technical solutions but also on the level of social acceptance, trust in institutions and citizens’ readiness to take part in processes of change. In the case of hydrogen technologies, significant differences are observed in the level of public knowledge, perceptions of the safety of the technology and readiness to accept investments related to the hydrogen economy. A further challenge is the growing influence of digital media and disinformation, which can shape social attitudes regardless of the actual level of knowledge about new energy technologies. Despite the growing importance of the social aspects of the energy transition, there is still a lack of tools for comprehensively diagnosing the level of social readiness for the energy transition and for effectively planning educational and communication activities supporting the development of the hydrogen economy. Research to date has focused mainly on technological and economic aspects, while issues related to building public awareness, resilience to disinformation and the use of modern analytical tools remain insufficiently understood. There seems to be a particularly important need to develop solutions for monitoring social attitudes and identifying the factors affecting the level of acceptance of hydrogen technologies.

The aim of the planned research is to identify the factors determining the level of public awareness and acceptance of hydrogen technologies, to develop a model for building public awareness supporting the development of hydrogen technologies and to create a system supporting social readiness for the energy transition. The project also involves assessing the possibilities of using data analysis methods and artificial intelligence tools in diagnosing social attitudes and designing educational and communication activities.

These goals will be achieved in stages. In the first stage, a review of the Polish and international scientific literature on the energy transition, social acceptance of new technologies, social communication and the use of artificial intelligence in social research will be carried out. At the same time, a research methodology will be developed, including the selection of research variables and indicators of social readiness for the energy transition.

The next stage will involve empirical research using quantitative and qualitative methods. Surveys, in-depth interviews and an analysis of data from digital and social media will be carried out. The data collected will make it possible to identify the level of public knowledge about hydrogen technologies, assess the degree of acceptance of new energy technologies and determine the influence of social, educational and communication factors on transformation processes.

The data collected will then be analysed using statistical methods and artificial intelligence tools supporting sentiment analysis, the identification of dominant social narratives and the recognition of disinformation. On the basis of the results, a model of social readiness for the energy transition will be developed, together with a set of recommendations on educational, information and communication activities.

The final stage of the project will involve developing, implementing and verifying the “System supporting social readiness for the energy transition” in the activities of Polska Izba Wodoru. The system will support the monitoring of social attitudes, the planning of educational campaigns and the evaluation of the effectiveness of the organisation’s communication activities. The project will culminate in an assessment of the effectiveness of the solution developed and the preparation of recommendations enabling its use by industry organisations, energy companies, public administration and other entities involved in the energy transition.

d) expected results and their significance and impact for science and society

The research will make it possible to achieve the scientific goals of the project, in particular those related to identifying the factors affecting the level of public awareness of hydrogen technologies and social readiness for the energy transition. In the course of the work, a data set will be collected comprising the results of surveys, interviews and analyses of data from digital and social media. These data will make it possible to determine the level of public knowledge about the hydrogen economy, identify the factors affecting the acceptance of hydrogen technologies and assess the influence of social communication and disinformation on citizens’ attitudes. One of the key results of the project will be the development of a model of social readiness for the energy transition and the creation of the “System supporting social readiness for the energy transition”. This solution will make it possible to monitor changes in social attitudes, identify areas requiring educational activities and support communication processes related to the implementation of hydrogen technologies. Another important element of the project will be the development of recommendations for industry organisations, companies, public administration and educational institutions on building public awareness and countering disinformation in the energy transition. The significance of the proposed research for the development of science lies above all in deepening knowledge of the social determinants of the energy transition, the acceptance of new technologies and the processes by which social attitudes towards technological innovation are formed. The model of social readiness for the energy transition may form the basis for further research in the sociology of new technologies, social communication and the use of artificial intelligence in social research. The project thus fits in with current directions in the social sciences related to the analysis of transformation processes and the relationship between society and new technologies.

The project’s impact on the economy and society will be multidimensional. The research results can be used by industry organisations, energy companies, local government units and public administration in planning activities related to the energy transition. The solutions developed will help to increase the effectiveness of social communication, raise citizens’ level of knowledge and build social acceptance for low-emission technologies. As a result of the project, it will be possible to create a model supporting society’s informed participation in the energy transition and to increase the effectiveness of activities related to the development of the hydrogen economy in Poland.

Three scientific publications are also planned: after the second semester on the social aspects of the energy transition and hydrogen technologies, after the fourth on social communication and the analysis of attitudes towards new energy technologies, and after the sixth on the use of artificial intelligence in analysing social readiness for the energy transition.

The project provides for the publication of three scientific articles in scored journals on the MNiSW list (at least 70 points), after the second, fourth and sixth semesters respectively. Participation in three scientific conferences with posters or papers is also planned.

The doctoral thesis is planned to be submitted in September 2028.

Not applicable.

The programme provides for annual reporting, so each doctoral candidate is required to prepare a report on their work after each successive year of the project. The report is evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

The first, second and third years of the doctoral candidate’s work end with a set of documents comprising the report and the supervisors’ opinions, and an evaluation of this material. After the fourth year, a final report is prepared — also presented at the end of the model prepared for mechanical engineering. Here, the declaration that the doctoral thesis has been submitted is particularly important.

The doctoral thesis was submitted by the required deadline. A scan of the first page of the thesis is shown below.

The final report is the last report prepared by the doctoral candidate and submitted together with the supervisors’ opinions. It covers the entire project (all years), not just the last year. This report is also evaluated by the advisory team for the Implementation Doctorate Programme on the basis of an assessment prepared by the reviewer. Sample documents are included at the end of the model prepared for mechanical engineering. They should be treated as illustrative, showing which elements are necessary in the reports. The content of the reports presented in this publication is also illustrative and may differ from the scientific views of other researchers.

This set of reports and the evaluation close the doctoral candidate’s entire period of activity. It also marks the end of the work of the expert from the advisory team for the Implementation Doctorate Programme on this particular project. The team does not evaluate doctoral theses; the appointment of reviewers and the rest of the procedure are the responsibility of the relevant Discipline Councils.

As mentioned, this set is intended to help in preparing the application and reports and to show which elements are evaluated and how. It also responds to the objection repeatedly raised by applicants that the number of characters allowed is too small and that it is impossible to fit within it. The number is adequate when thoughts are expressed concisely and synthetically, which it in a way enforces, for the comfort of the team’s work. The application form does have an Attachments option, but it should be used only in exceptional situations, for example if someone wishes to support science–industry cooperation with a letter of intent (though in general this is not necessary). Treating the attachment as a place to add what does not fit in the form should be avoided. This rule was adopted, among other things, so that every applicant has an equal chance, which is why everyone must fit within the character limit — as in many other applications, e.g. for funding.

Of course, every application and report written is a little different. Each contains a unique idea for research and implementation, and each also reflects the different character of the person preparing it. Moreover — although the team evaluates applications and reports jointly on the basis of a presentation by a specific reviewer — every person (including every reviewer) is a little different and may pay attention to different aspects of the project. That is why the models presented can and should serve as templates, allowing the uninitiated to better understand the nature and requirements of the programme.

Of the models presented, the most extensive is the one for mechanical engineering. The others have less developed sections on annual and final reports. This is because most problems and non-conformities with the programme’s requirements are observed at the application stage, which is why we have paid particular attention to it. As for reports, if the application is handled correctly there are generally no disputes or doubtful situations; the most common comment on the first annual reports is that they are too brief, which improves in subsequent years.

The methodological assumptions underlying the statistical analysis are presented below. The analysis was based on raw project data obtained from the National Information Processing Institute – National Research Institute (OPI PIB). The data set from the Implementation Doctorate Programme contains information on the projects carried out under the programme. It includes detailed data on the number of projects accepted in each edition and analyses which universities and PAN institutes run the largest numbers of these projects. It also contains geographical information, such as the location of universities and of the entities cooperating on the projects, the industries represented by these entities, and data on the leaders in carrying out projects and the companies involved in the programme. The data set also covers the gender of applicants and an analysis of the fields and disciplines of science to which the projects belong. In addition, it contains details of the most frequently recurring keywords associated with the projects. Data for editions 1–7 were obtained from OPI in a single file, and data for edition 8 alone were then received in a somewhat different format, so not all analyses could be carried out jointly, and some data are presented for editions 1–7 only. The indicators and the sections in which they are presented:

The table below shows the number of projects accepted by the Ministry of Science and Higher Education in successive editions of the Implementation Doctorate Programme.

Chart 4.1Number of projects accepted by edition

385

461

364

588

476

440

167

171

1<br>(2017)

2 (2018)

3 (2019)

4 (2020)

5 (2021)

6 (2022)

7 (2023)

8 (2024)

The table shows the number of projects accepted under the programme in each edition from 2017 to 2024. In total, 3,052 projects were approved over the eight years. The number of accepted projects varied, but until 2020 it remained at a relatively high level. The programme started in 2017 with 385 approved projects, and in 2018 there was a clear increase to 461. In 2019 the number fell to 364, but in 2020 the highest number of accepted projects in the entire period was recorded — 588. A downward trend began in 2021. In 2021, 476 projects were accepted, in 2022 — 440, and in 2023 — just 167, the lowest result of all editions. This last drop is particularly significant, as it represents an almost threefold decrease compared with the record year 2020. In the most recent year, 2024, 171 projects were accepted.

The table below shows the universities and programme participants.

The chart below shows the number of programme participants by university, including only those with more than 50 participants. In total, data are presented for 13 universities, among which technical universities clearly dominate. The largest number of programme participants came from the Silesian University of Technology, with 470 participants. Next were AGH University of Krakow — 387 participants — and Warsaw University of Technology — 372 participants. High participation was also recorded at Wrocław University of Science and Technology (199), Gdańsk University of Technology (126) and Poznan University of Technology (124). Next, a significant number of participants came from Cracow University of Technology (99) and Lodz University of Technology (94). Classic universities were at the end of the list — Wroclaw University of Economics and Business (60), Jagiellonian University in Kraków (56), Adam Mickiewicz University, Poznań (53), and the Medical University of Silesia in Katowice and the University of Silesia in Katowice (52).

Chart 4.2Number of participants by university, for universities with more than 50 participants

Silesian University of Technology

470

AGH University of Krakow

387

Warsaw University of Technology

372

Wrocław University of Science and Technology

199

Gdańsk University of Technology

126

Poznan University of Technology

124

Cracow University of Technology

99

Lodz University of Technology

94

Wroclaw University of Economics and Business

60

Jagiellonian University in Kraków

56

Adam Mickiewicz University, Poznań

53

Medical University of Silesia in Katowice

52

University of Silesia in Katowice

52

Chart 4.3Number of participants by university, for universities with 21 to 49 participants

West Pomeranian University of Technology in Szczecin

49

Rzeszow University of Technology

49

University of Warsaw

49

Opole University of Technology

43

Wrocław University of Environmental and Life Sciences

42

University of Lodz

42

SGH Warsaw School of Economics

41

Poznań University of Life Sciences

34

Nicolaus Copernicus University in Toruń

33

WSB University

28

Robert Szewalski Institute of Fluid-Flow Machinery, Polish Academy of Sciences

25

Military University of Technology

25

University of Life Sciences in Lublin

24

Włodzimierz Trzebiatowski Institute of Low Temperature and Structure Research, Polish Academy of Sciences

23

Poznań University of Economics and Business

23

University of Wrocław

23

The chart shows the number of programme participants by university and research institution, for those with 21 to 49 participants.

The list includes 16 entities, including universities, technical universities and research institutes. The largest numbers of participants in this group came from the West Pomeranian University of Technology in Szczecin, Rzeszow University of Technology and the University of Warsaw — 49 each. Four universities have 41–43 participants: Opole University of Technology, Wrocław University of Environmental and Life Sciences, the University of Lodz and SGH Warsaw School of Economics. Slightly fewer participants came from Poznań University of Life Sciences (34) and Nicolaus Copernicus University in Toruń (33). Next, WSB University has 28 participants.

Chart 4.4Number of participants by university, for universities with 1 to 20 participants

Bialystok University of Technology

19

Poznan University of Medical Sciences

19

Medical University of Lodz

19

Institute of Fundamental Technological Research, Polish Academy of Sciences

16

University of Warmia and Mazury in Olsztyn

15

Kielce University of Technology

14

University of Szczecin

14

Medical University of Warsaw

14

University of Agriculture in Krakow

14

Kozminski University

11

Warsaw University of Life Sciences

11

Bydgoszcz University of Science and Technology

10

University of Gdańsk

10

Medical University of Gdańsk

9

Bohdan Dobrzański Institute of Agrophysics, Polish Academy of Sciences

9

Henryk Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences

9

Aleksander Krupkowski Institute of Metallurgy and Materials Science, Polish Academy of Sciences

9

Polish-Japanese Academy of Information Technology

9

SWPS University

8

Kazimierz Wielki University in Bydgoszcz

7

Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences

5

University of Zielona Góra

5

Eugeniusz Geppert Academy of Art and Design in Wrocław

4

Systems Research Institute, Polish Academy of Sciences

4

Institute of Human Genetics, Polish Academy of Sciences

4

Lublin University of Technology

4

Maria Curie-Skłodowska University in Lublin

4

Wroclaw Medical University

4

Medical University of Lublin

4

Czestochowa University of Technology

3

Jan Kochanowski University of Kielce

3

Institute of Physical Chemistry, Polish Academy of Sciences

3

The group with 23 to 25 participants includes institutions such as the Military University of Technology, Instytut Maszyn Przepływowych PAN, the University of Life Sciences in Lublin, Instytut Niskich Temperatur i Badań Strukturalnych, Instytut Chemii Fizycznej Polskiej Akademii Nauk, Poznań University of Economics and Business and the University of Wrocław. The largest representation (19 participants each) came from Bialystok University of Technology, Poznan University of Medical Sciences and the Medical University of Lodz. Instytut Podstawowych Problemów Techniki PAN has 16 participants. High participation was also recorded at the University of Warmia and Mazury in Olsztyn (15) and at several universities with 14 participants each, including the University of Szczecin, the Medical University of Warsaw, the University of Agriculture in Krakow and Kielce University of Technology. The group of 9–11 includes, among others, the University of Gdańsk, the Medical University of Gdańsk, Warsaw University of Life Sciences, Kozminski University and several institutes of the Polish Academy of Sciences. The smallest numbers of participants (3–5) were recorded at, among others, Instytut Chemii Fizycznej PAN, Jan Kochanowski University of Kielce, Czestochowa University of Technology and the University of Zielona Góra.

In summary, the Implementation Doctorate Programme attracted the greatest interest among students of technical universities — which is directly related to its practical and engineering profile. Medical, life sciences and economics universities also showed significant involvement, though to a lesser extent. The presence of PAN research institutes and art academies shows that the programme is interdisciplinary and open. The presence of institutions from almost all of Poland (from large academic cities to smaller centres) proves that the programme has a wide geographical reach and is not limited to the leading universities.

The breakdown of universities and numbers of participants by voivodeship is presented below.

Chart 4.5Universities and PAN institutes carrying out the most projects

Silesian

605

Lesser Poland

574

Masovian

563

Lower Silesian

360

Greater Poland

257

Pomeranian

170

Łódź

155

West Pomeranian

63

Kuyavian-Pomeranian

50

Subcarpathian

49

Lublin

45

Opole

43

Podlaskie

19

Świętokrzyskie

17

Warmian-Masurian

15

Lubusz

5

Map 4.1Universities and PAN institutes carrying out the most projects

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

The data show the number of programme participants by voivodeship, revealing considerable regional variation. The largest number of participants came from the Silesian Voivodeship, with 605. Slightly fewer took part from the Lesser Poland (574) and Masovian (563) voivodeships, which indicates particularly high activity in these regions. Significant participation was also recorded in the Lower Silesian (360) and Greater Poland (257) voivodeships. The Pomeranian (170) and Łódź (155) voivodeships were in the middle range, while fewer than 100 participants were recorded in voivodeships such as West Pomeranian (63), Kuyavian-Pomeranian (50), Subcarpathian (49), Lublin (45) and Opole (43). The lowest involvement was shown by the Podlaskie (19), Świętokrzyskie (17) and Warmian-Masurian (15) voivodeships and by Lubusz, which, with 5 participants, came last. This distribution suggests that the programme attracted the greatest interest in regions with large academic and industrial centres, while smaller voivodeships, especially in the east and north of the country, showed lower activity. This may be due to differences in educational potential, access to information or the specific character of local universities.

The distribution of programme participants by voivodeship — from very high in regions such as Silesian, Lesser Poland and Masovian to very low in voivodeships such as Lubusz, Warmian-Masurian and Świętokrzyskie — is not accidental. Several key reasons can be identified that may explain these differences:

1. Number and size of universities

The voivodeships with the highest shares (Silesian, Lesser Poland, Masovian, Greater Poland, Lower Silesian) are the regions with the largest and most developed academic centres in Poland. They are home to technical, medical and general universities with large numbers of students and academic staff. For example:

• –Silesian: Silesian University of Technology, University of Silesia,
• –Lesser Poland: AGH, Jagiellonian University,
• –Masovian: Warsaw University of Technology, University of Warsaw,
• –Lower Silesian: Wrocław University of Science and Technology, University of Wrocław,
• –Greater Poland: Poznan University of Technology, Adam Mickiewicz University.

These institutions naturally generate a larger number of potential programme participants.

2. The nature of the programme — likely technical or practical

The Implementation Doctorate Programme is geared towards developing engineering, technological, digital or entrepreneurial competences, so it naturally resonated more strongly with technical universities, which dominate precisely in the largest centres. Smaller voivodeships often have universities with a more regional profile or specialising in the social sciences and education, which may not match the programme’s subject matter.

3. Access to information and academic networks

Large academic centres have better information structures, careers offices, project promotion departments, student unions and organisations that effectively spread information about available initiatives. At smaller universities, these channels may be weaker and the promotion of the programme less intensive or not a priority.

4. Activity of local staff and institutions

Nationwide programmes often rely on cooperation with partner universities. Where local academic staff were more involved in promoting it, the results were greater. The lack of a strong local project leader in smaller voivodeships may have translated into low participant numbers.

5. Socio-economic factors

Regions with higher levels of urbanisation, investment in education and a knowledge-based economy (e.g. Warszawa, Kraków, Wrocław, Poznań, Katowice) take part in development projects more often. Eastern and north-western voivodeships (e.g. Lubusz, Warmian-Masurian, Świętokrzyskie) have a lower density of universities, smaller academic cities and less access to social capital, which may affect their lower level of involvement.

6. The effect of recommendations and networks

If the programme was rated highly by participants from leading universities in the first editions, this may have created a snowball effect — recommendations, growing interest and a larger inflow of applicants from the same universities and regions in subsequent years.

The distribution of participants reflects above all the availability and institutional potential of universities in a given region, the thematic profile of the programme, the strength of promotion and local involvement, as well as demographic and economic factors. This points to the need for a more differentiated information and recruitment strategy if the programme is to reach students from all over Poland more evenly.

The table below shows the number of qualified participants by edition and gender.

Chart 4.6Number of participants qualified for the programme by gender and edition (excluding missing gender data)

115

270

110

350

93

269

156

429

140

335

148

292

59

108

57

114

1<br>(2017)

2 (2018)

3 (2019)

4 (2020)

5 (2021)

6 (2022)

7 (2023)

8 (2024)

Chart 4.7Number of participants qualified for the programme by gender and edition (excluding missing gender data)

1 · 2017

30%

70%

2 (2018

24%

76%

3 (2019

26%

74%

4 (2020

27%

73%

5 (2021

29%

71%

6 (2022

34%

66%

7 (2023

35%

65%

8 (2024

33%

67%

The data on the number of programme participants by gender in 2017–2024 show clear differences between the participation of women and men. In every year, men made up the majority of participants. In 2017 there were 115 women and 270 men. In subsequent editions the difference remained at a similar level — in 2018 the number of women fell slightly to 110, while the number of men rose to 350. In 2019 there were 93 women and 269 men. The year 2020 brought an increase both in the total number of participants and in the number of women — to 156 — but men still dominated (429). In 2021 and 2022 the number of women was 140 and 148 respectively, and the number of men 335 and 292. The 2023 edition had only 59 women and 108 men, a significant drop for both groups but particularly noticeable among men. The situation was similar the following year: 57 women and 114 men. In total, 878 women and 2,167 men took part in the programme in 2017–2023, meaning that women made up about 30% of participants and men 70%. This distribution suggests a clear predominance of men in all editions of the programme and may be the basis for further analysis of barriers or differences in interest in the programme between the sexes.

The chart shows several important elements, among which the following are worth noting:

• –an increase in participants to a peak in 2020 (588),
• –followed by a gradual decline to the lowest number in 2023 (167).

Chart 4.8Trend in the number of programme participants in successive years

The line chart above shows the trend in the number of participants in 2017–2024.

In summary, the number of programme participants changed dynamically in 2017–2024. The programme started in 2017 with 385 participants, and in 2018 the number had already risen to 461. The following year, 2019, it fell to 364. In 2020 the programme reached its peak popularity, attracting as many as 588 people — the most in the entire period analysed. In 2021 and 2022 the number of participants gradually decreased, to 476 and 440 respectively. In 2023, however, there was a drastic drop to 167, the lowest result in the entire seven-year period. The following year there were only slightly more participants — 171.

It is also worth mentioning that the decrease in the annual number of doctorates awarded resulted from the total amount of funding that the Ministry of Science and Higher Education decided to allocate to new applications. This decision raised the score threshold that applications must reach to obtain funding.

The table below shows the fields of science with the number of projects in editions 1–8, by edition.

Chart 4.9Number of participants by field of science (excluding “no data”)

Engineering and technical sciences

1,418

Technical sciences

564

Natural and exact sciences

311

Social sciences

267

Agricultural sciences

130

Medical and health sciences

100

Economic sciences

70

Chemical sciences

65

Medical sciences

37

Humanities

26

Physical sciences

20

Earth sciences

17

Pharmaceutical sciences

13

Biological sciences

12

Mathematical sciences

6

Arts

4

Fine arts

4

Legal sciences

3

Below, the areas are grouped according to the current division into fields.

Chart 4.10Number of participants by field of science (excluding “no data”)

Engineering and technical sciences (including technical sciences up to edition 2)

1,978

Natural and exact sciences (Earth, mathematical, physical, chemical and biological sciences up to edition 2)

429

Social sciences (including legal and economic sciences up to edition 2)

339

Medical and health sciences (including medical and pharmaceutical sciences up to edition 2)

150

Agricultural sciences

130

Humanities

26

Arts (fine arts up to edition 2)

8

The data show the number of programme participants by field of science and reveal a clear dominance of technical and exact fields. The largest group were participants from engineering and technical sciences — as many as 1,978, a clear majority, which confirms the dominance of practical and technological fields. Natural and exact sciences (429 participants) and social sciences (339) also had large shares, the latter being the only non-engineering field to exceed 200 participants. Agricultural sciences (130) and medical and health sciences (150) also had significant shares, indicating interest in areas related to biotechnology, health and nature. The least represented were the humanities (26) and the arts (4). Number of participants by field of science (excluding “no data”)

Chart 4.11Fields of science of the doctorates and the gender of applicants

Engineering and technical sciences (including technical sciences up to edition 2)

384

1,594

Natural and exact sciences (Earth, mathematical, physical, chemical and biological sciences up to edition 2)

184

245

Social sciences (including legal and economic sciences up to edition 2)

131

208

Medical and health sciences (including medical and pharmaceutical sciences up to edition 2)

88

62

Agricultural sciences

72

58

Humanities

16

10

Arts (fine arts up to edition 2)

6

2

The data show that men dominated in every field, although the share of women was significant in several areas. The largest number of participants was recorded in engineering and technical sciences (also including technical sciences in the first editions), with a total of 1,978 participants, including 1,594 men and 384 women. Despite the clear predominance of men, the number of women in this field was also significant. The second largest field was natural and exact sciences, with 429 participants — 245 men and 184 women. This group had one of the highest relative shares of women. In the social sciences (including legal and economic sciences up to edition 2) there were 339 participants, of whom 131 were women and 208 men. Medical and health sciences (including the medical and pharmaceutical sciences of the first editions) had a total of 150 participants — 88 women and 62 men — making this the only field in which women outnumbered men. Agricultural sciences also showed a strong share of women: 72 women compared with 58 men, the second field with a majority of women. In the humanities and the arts (including fine arts), the participation of women was moderate (16 and 6 women respectively) but still higher than that of men (10 and 2).

Conclusions

• –Most participants represented technical and exact fields, but only about 19% of participants in engineering and technical sciences were women, which indicates a large gender imbalance.
• –Fields such as medicine, agriculture, the humanities and the arts have a relatively higher share of women.
• –It is clear that the structure of participants reflects the classic patterns of women’s and men’s choice of fields of study, but it also shows that women are increasingly involved in exact and technical fields.

The table below shows the disciplines of the participants in successive editions of the programme.

The breakdown of participants for editions 1 to 8 is presented below, reflecting the new names of disciplines from 2022 and taking into account the divisions and names in force in 2011–2018 and 2018–2022. The Regulation of the Minister of Education and Science of 11 October 2022 on fields of science, scientific disciplines and artistic disciplines retained the two-level division into fields and disciplines. The regulation entered into force on 11 November 2022. It introduced a new field and discipline of family studies and the discipline of biblical studies. Veterinary sciences again became a separate field, and in engineering and technical sciences “space technologies” were added to the group “automation, electronics and electrical engineering”.

1. Humanities

• –archaeology
• –ethnology and cultural anthropology
• –philosophy
• –history
• –linguistics
• –literary studies
• –cultural and religious studies
• –arts studies
• –Polish studies

2. Engineering and technical sciences

• –architecture and urban planning
• –automation, electronics, electrical engineering and space technologies
• –information and communication technology
• –safety engineering
• –biomedical engineering
• –chemical engineering
• –civil engineering, geodesy and transport
• –materials engineering
• –mechanical engineering
• –environmental engineering, mining and energy
• –heritage protection and conservation of monuments

3. Medical and health sciences

• –medical biology
• –pharmaceutical sciences
• –medical sciences
• –physical culture sciences
• –health sciences

4. Family studies

• –family studies

5. Agricultural sciences

• –forestry sciences
• –agriculture and horticulture
• –food and nutrition technology
• –animal science and fisheries

6. Social sciences

• –economics and finance
• –socio-economic geography and spatial management
• –security studies
• –social communication and media studies
• –political science and public administration
• –management and quality studies
• –legal sciences
• –sociology
• –pedagogy
• –canon law
• –psychology
• –international relations

7. Natural and exact sciences

• –astronomy
• –biotechnology
• –computer science
• –mathematics
• –biological sciences
• –chemical sciences
• –physical sciences
• –Earth and environmental sciences

8. Theology

• –biblical studies
• –theology

9. Veterinary sciences

• –veterinary medicine

10. Arts

• –film and theatre arts
• –musical arts
• –fine arts and conservation of works of art

1. Humanities

• –archaeology
• –philosophy
• –history
• –linguistics
• –literary studies
• –cultural and religious studies
• –arts studies

2. Engineering and technical sciences

• –architecture and urban planning
• –automation, electronics and electrical engineering
• –information and communication technology
• –biomedical engineering
• –chemical engineering
• –civil engineering and transport
• –materials engineering
• –mechanical engineering
• –environmental engineering, mining and energy

3. Medical and health sciences

• –pharmaceutical sciences
• –medical sciences
• –physical culture sciences
• –health sciences

4. Agricultural sciences

• –forestry sciences
• –agriculture and horticulture
• –food and nutrition technology
• –veterinary medicine
• –animal science and fisheries

5. Social sciences

• –economics and finance
• –socio-economic geography and spatial management
• –security studies
• –social communication and media studies
• –political science and public administration
• –management and quality studies
• –legal sciences
• –sociology
• –pedagogy
• –canon law
• –psychology

6. Natural and exact sciences

• –astronomy
• –computer science
• –mathematics
• –biological sciences
• –chemical sciences
• –physical sciences
• –Earth and environmental sciences

7. Theology

• –theology

8. Arts

• –film and theatre arts
• –musical arts
• –fine arts and conservation of works of art

1. Humanities area

Humanities

• –archaeology
• –library and information science
• –ethnology
• –philosophy
• –history
• –art history
• –linguistics
• –cultural studies
• –literary studies
• –family studies
• –arts studies
• –management science
• –religious studies

Theology

2. Social sciences area

Social sciences

• –security studies
• –defence studies
• –media studies
• –political science
• –public policy
• –cognitive science and social communication
• –pedagogy
• –psychology
• –sociology

Economic sciences

• –economics
• –finance
• –management science
• –commodity science

Legal sciences

• –public administration
• –law
• –canon law

3. Exact sciences area

Mathematical sciences

• –mathematics
• –computer science

Physical sciences

• –astronomy
• –biophysics
• –physics
• –geophysics

Chemical sciences

• –biochemistry
• –biotechnology
• –chemistry
• –environmental protection
• –chemical technology

4. Natural sciences area

Biological sciences

• –biochemistry
• –biophysics
• –biology
• –biotechnology
• –ecology
• –microbiology
• –environmental protection

Earth sciences

• –geophysics
• –geography
• –geology
• –oceanology

5. Technical sciences area

Technical sciences

• –architecture and urban planning
• –automation and robotics
• –biocybernetics and biomedical engineering
• –biotechnology
• –machine construction and operation
• –civil engineering
• –power engineering
• –geodesy and cartography
• –mining and engineering geology
• –computer science
• –chemical engineering
• –materials engineering
• –production engineering
• –agricultural engineering
• –environmental engineering
• –mechanics
• –metallurgy
• –chemical technology
• –telecommunications
• –transport
• –textile engineering

6. Agricultural, forestry and veterinary sciences area

Agricultural sciences

• –agronomy
• –biotechnology
• –agricultural engineering
• –environmental protection and management
• –horticulture
• –fisheries
• –food and nutrition technology
• –animal science

Forestry sciences

• –wood technology
• –forestry

Veterinary sciences

7. Medical and health sciences area

• –medical biology
• –medicine
• –dentistry

Pharmaceutical sciences

Health sciences

Physical culture sciences

8. Arts area

Film arts

Musical arts

• –conducting
• –instrumental performance
• –composition and music theory
• –sound engineering
• –eurhythmics and dance
• –vocal performance

Fine arts

• –fine arts
• –design
• –conservation and restoration of works of art

Theatre arts

Names of disciplines with the assigned field of science

The breakdown and number of participants for individual fields of science are presented below.

Chart 4.12Number of participants in the humanities (editions 2–8)

Arts

8

Cultural and religious studies

6

Linguistics

3

Literary studies

3

History

2

Archaeology

1

Philosophy

1

Cultural studies

1

Ethnology and cultural anthropology

1

In the humanities, participants are unevenly distributed across disciplines. The least interest was shown in archaeology, philosophy, ethnology and cultural anthropology, and cultural studies, each attracting only 1 participant. History attracted slightly more interest, with 2 participants. Linguistics and literary studies attracted a medium level of interest, with 3 participants each. Slightly more participants chose cultural and religious studies and arts studies — 6 in each of these disciplines — which indicates their relatively high popularity compared with the other humanities disciplines. Arts studies are represented by 8 participants.

Chart 4.13Number of participants in engineering and technical sciences (including technical sciences) — editions 1–8

Mechanical engineering

343

Materials engineering

290

Environmental engineering, mining and energy

229

Information and communication technology

167

Automation, electronics and electrical engineering

159

Chemical engineering

130

Machine construction and operation

78

Civil engineering and transport

75

Chemical technology

69

Automation and robotics

52

Energy

41

Mechanics

34

Biomedical engineering

33

Electronics

28

Automation, electronics, electrical engineering and space technologies

27

Mining and engineering geology

27

Environmental engineering

21

Telecommunications

21

Metallurgy

20

Civil engineering, geodesy and transport

19

Architecture and urban planning

18

Production engineering

10

Electrical engineering

7

Transport

6

Biocybernetics and biomedical engineering

4

Civil engineering

4

In engineering and technical sciences, the number of participants varied greatly depending on the discipline. In total, the list includes 1,912 participants representing 26 disciplines. The most represented discipline is mechanical engineering, with 343 participants. Next come materials engineering (290 participants) and environmental engineering, mining and energy (229 participants). Information and communication technology (167) and automation, electronics and electrical engineering (159) also have high numbers of participants, indicating strong interest in modern technologies and technical systems. Disciplines such as chemical engineering (130), machine construction and operation (78), civil engineering and transport (75) and chemical technology (69) are of medium size. This group makes up a significant part of all participants and reflects a stable interest in classic engineering fields. Disciplines such as automation and robotics (52), power engineering (41), mechanics (34) and biomedical engineering (33) are less represented. The smallest groups are in biocybernetics and biomedical engineering and civil engineering (4 each), as well as transport (6) and electrical engineering (7). These results show candidates’ clear preference for practical technical fields widely used in industry.

Chart 4.14Number of participants in natural and exact sciences (including biological, chemical, physical, mathematical and Earth sciences) — editions 1–8

Chemical sciences

187

Computer science

104

Chemistry

60

Physical sciences

37

Biological sciences

33

Earth and environmental sciences

31

Biotechnology

18

Physics

18

Geology

10

Mathematics

4

Biochemistry

3

Geophysics

3

Astronomy

2

Geography

2

Oceanology

2

Biology

1

The table shows the structure of participants by discipline within natural and exact sciences. Chemical sciences have the largest share, with 187 participants. Computer science (104) and chemistry (60) are also well represented, confirming strong interest in these fields. Physical sciences (37), biological sciences (33) and Earth and environmental sciences (31) have moderate numbers of participants. Smaller groups are formed by biotechnology and physics (18 each) and geology (10). The least represented are disciplines such as biology, astronomy, geography, oceanology, biochemistry, geophysics and mathematics, each with no more than a few participants. The data show clear disproportions in size between individual disciplines.

Chart 4.15Number of participants in medical and health sciences (editions 1–8)

Medical sciences

63

Pharmaceutical sciences

31

Medical biology

21

Medicine

16

Health sciences

5

Physical culture sciences

1

In medical and health sciences, the number of participants varied greatly between disciplines. The fewest chose physical culture sciences (only 1) and health sciences (5). Medical biology attracted more interest, with 21 participants. Pharmaceutical sciences attracted even more, with 31. The most popular in this group, however, were medical sciences, chosen by 63 participants, making them the most frequently chosen discipline within medical and health sciences.

Chart 4.16Number of participants in agricultural sciences (editions 1–8)

Food and nutrition technology

56

Agriculture and horticulture

24

Animal science and fisheries

17

Forestry sciences

10

Agronomy

5

Horticulture

2

In agricultural sciences, participants’ interest was unevenly distributed across disciplines. The fewest chose horticulture (2), agronomy (5) and forestry sciences (10), and slightly more animal science and fisheries (17). Agriculture and horticulture attracted more interest, with 24 participants. The most, however, chose food and nutrition technology — 56 participants — making it the most popular discipline in this group. These results show that fields related to food processing and quality currently attract the most attention within agricultural sciences.

Chart 4.17Number of participants in social sciences (editions 1–8)

Management and quality studies

187

Economics

38

Economics and finance

24

Management science

23

Legal sciences

18

Political science and public administration

9

Finance

9

Sociology

8

Security studies

8

Psychology

8

Socio-economic geography and spatial management

5

Law

3

Pedagogy

2

Social communication and media studies

2

Sociology

2

The data in the table show the distribution of participants across disciplines in the social, economic and legal sciences. The total number of people in the list is 346, with significant differences in the scale of representation of individual areas. The largest group are representatives of management and quality studies, numbering 187. This discipline clearly dominates the others. Next come economics (38 participants), economics and finance (24) and management science (23), which confirms the important role of economic and managerial issues. The remaining disciplines are much smaller. Legal sciences have 18 participants, while areas such as psychology, sociology, security studies, finance and political science and public administration are represented by groups of 8 to 9. The lowest values were recorded for pedagogy, sociology and social communication and media studies, as well as for law and socio-economic geography and spatial management.

Chart 4.18Number of participants in veterinary sciences (editions 4–8)

Number of participants

13

In veterinary sciences there are only 13 participants, from editions 3, 4 and 5.

Chart 4.19Number of participants in the arts (editions 2, 5, 6, 8)

Fine arts and conservation of works of art

3

Fine arts

2

Design

2

Film and theatre arts

1

In summary, the total number of participants across all fields is as follows:

Chart 4.20Most popular scientific disciplines, editions 1–8

Mechanical engineering

343

Materials engineering

290

Environmental engineering, mining and energy

229

Chemical sciences

187

Management and quality studies

187

Information and communication technology

167

Automation, electronics and electrical engineering

159

Chemical engineering

130

Computer science

104

Machine construction and operation

78

By far the largest share belongs to engineering and technical sciences, with 1,912 participants, confirming their dominant position. Next in size are natural and exact sciences (515 participants) and social sciences (346). Much smaller representation was recorded in medical and health sciences (137) and agricultural sciences (114). The remaining areas, such as the humanities, veterinary sciences and the arts, include only a small number of participants, not exceeding a few dozen. The data show a clear predominance of technical and exact areas over the other fields of science.

Project keywords

The input data contain 15,437 keywords. Given the very large number of keywords, the table below presents 80 records showing the keywords that appeared in applications at least 10 times.

Chart 4.21Most frequent keywords (20 or more)

Artificial intelligence

132

Machine learning

131

Optimisation

90

Recycling

38

Safety

36

Energy

36

Neural networks

35

Modelling

33

Data science

32

3D printing

32

Corrosion

31

Management

30

Composites

29

Sustainable development

29

Renewable energy sources

28

Data analysis

25

Automation

25

Diagnostics

25

Rail vehicles

24

Industry 4.0

24

Project management

24

Microstructure

23

Natural language processing

22

Energy efficiency

21

Polymers

21

Hydrogen

20

The most frequent keywords concern modern technologies and data processing. Machine learning (131 occurrences) and artificial intelligence (132) clearly dominate, indicating strong interest in AI and automation. In third place is optimisation (90), also widely used in engineering, computer science and process management. Next come topics related to sustainable development and modern industry, such as recycling (38), safety (36), energy (36), neural networks (35), modelling (33), data science (32) and corrosion (31). Industrial technologies are also gaining popularity, including 3D printing (32).

The rest of the list shows significant interest in management and environmental topics: management (30), sustainable development, composites (29) and renewable energy sources (28). At a similar level are technical concepts such as automation and Industry 4.0, as well as data analysis, diagnostics, rail vehicles and project management (24–25). The list closes with specialised topics, including microstructure (23), natural language processing (22) and energy efficiency (21), which, although less frequent, also show how topical the subjects are. Recently, topics related to polymers and hydrogen have been gaining importance.

Overall, this shows strong interest in modern technologies, the industry of the future, data analysis and sustainable development.

Looking at how often individual keywords appeared in successive editions, one can observe the interests of Implementation Doctorate Programme participants over time. The 10 most frequent keywords were used for the analysis.

Chart 4.22Occurrences of the keyword “artificial intelligence” in successive editions

7

5

12

34

21

28

12

13

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.23Occurrences of the keyword “machine learning” in successive editions

12

11

12

31

24

21

9

11

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

The number of implementation doctorates in artificial intelligence and machine learning (with the keywords “artificial intelligence” and “machine learning”) grew from the first editions of the programme, reaching its highest level in the fourth edition. After this peak there are fluctuations, but in subsequent editions interest in AI and machine learning remains at a clearly high level, confirming their growing importance in implementation doctorates.

Chart 4.24Occurrences of the keyword “recycling” in successive editions

1

2

0

11

9

8

6

1

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.25Occurrences of the keyword “safety” in successive editions

1

3

7

8

7

7

0

3

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.26Occurrences of the keyword “energy” in successive editions

10

8

4

10

4

0

0

0

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.27Occurrences of the keyword “neural networks” in successive editions

1

2

8

8

6

7

2

1

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.28Occurrences of the keyword “modelling” in successive editions

4

10

5

3

4

3

1

3

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.29Occurrences of the keyword “data science” in successive editions

18

10

0

3

1

0

0

0

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Chart 4.30Occurrences of the keyword “3D printing” in successive editions

3

2

7

6

8

1

4

1

Edition 1

Edition 2

Edition 3

Edition 4

Edition 5

Edition 6

Edition 7

Edition 8

Entities (employers) in which implementation doctorates are carried out

Throughout the programme, the vast majority of entities were indicated by individual participants only, which made it impossible to build indicator 10 for editions 1–4. Because of differences in the form of entries, such as extra spaces, special characters or different address formats, entity names were standardised on the basis of registered office data and the NIP tax number, and in case of doubt the POL-on and GUS registers were consulted.

The most active cooperating entities are shown in the table below.

Chart 4.31Entities (employers) with the most programme participants (more than 5)

Główny Urząd Miar

21

Łukasiewicz Research Network – Institute of Non-Ferrous Metals

18

Polski koncern naftowy orlen spółka akcyjna z siedzibą w płocku

15

Łukasiewicz Research Network – Institute of Microelectronics and Photonics

14

Łukasiewicz Research Network – Poznań Institute of Technology

14

Łukasiewicz Research Network – Institute of Aviation

13

Łukasiewicz Research Network – PORT Polski Ośrodek Rozwoju Technologii

10

Celon pharma s.a.

9

Łukasiewicz Research Network – Institute of Heavy Organic Synthesis “Blachownia”

9

Łukasiewicz Research Network – Krakow Institute of Technology

8

Nokia solutions and networks sp. z o.o.

7

Ponar wadowice s.a.

7

Atos poland global services sp. z o. o.

6

Atos poland r&d sp. z o. o.

6

General electric company polska sp. z o.o

6

Grupa azoty zakłady azotowe kędzierzyn s.a.

6

Hutchinson poland sp. z o.o.

6

KOMAG Institute of Mining Technology

6

Nexteer automotive poland sp. z o.o.

6

National Information Processing Institute – National Research Institute

6

Pit-radwar s.a.

6

Ryvu therapeutics s.a.

6

Łukasiewicz Research Network – Institute of Ceramics and Building Materials

6

Łukasiewicz Research Network – Stanisław Staszic Institute for Ferrous Metallurgy

6

The list of entities recurring more than 5 times is dominated by research institutes, especially those belonging to the Łukasiewicz Research Network, which shows their key role in the national R&D ecosystem. The most frequent are Główny Urząd Miar (21 doctoral candidates), Instytut Metali Nieżelaznych (18), PKN ORLEN (15), Instytut Mikroelektroniki i Fotoniki and Poznański Instytut Technologiczny (14 each) and Instytut Lotnictwa (13). Industrial entities such as ATOS, General Electric, Nokia, Nexteer and Grupa Azoty also have a significant share, reflecting the private sector’s strong commitment to innovation.

The list also shows strong representation of the technology, chemical, materials, automotive and energy industries. Centres in Warszawa, Kraków, Gliwice and Wrocław appear most often, indicating the concentration of R&D infrastructure there on a national scale.

Map 4.2Total implementation doctorates in companies, editions 5–7, entities with at least 3 doctorates

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

In the breakdown of doctoral candidates (at least 3 doctorates per company) by voivodeship, the Masovian Voivodeship clearly dominates, with 154 doctoral candidates, far ahead of the other regions. Next come Silesian (75), Lesser Poland (55) and Greater Poland (33), indicating a strong concentration of doctoral education in the country’s largest academic and research centres. Lower Silesian (25), Opole (18), Kuyavian-Pomeranian (17), Lublin (8) and Łódź (6) have moderate values, while Pomeranian and Świętokrzyskie have only 4 doctoral candidates each. The Lubusz, Subcarpathian, Podlaskie, Warmian-Masurian and West Pomeranian voivodeships have no companies running at least 3 doctorates.

These data point to a strong polarisation of research activity in Poland, concentrated around large metropolitan areas.

Map 4.3Number of entities with at least 3 doctorates, editions 5–7

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

A clear concentration of doctoral activity in a few regions can be seen. Most such institutions are in the Masovian (25), Silesian (14) and Lesser Poland (12) voivodeships, which shows that research activity in these voivodeships is widely spread and that many different centres are involved in doctoral education. Greater Poland (7) and Lower Silesian (5) also have high values, even though they are not leaders in the total number of doctoral candidates. The Opole, Kuyavian-Pomeranian, Lublin, Łódź, Świętokrzyskie and Pomeranian voivodeships have 1–2 entities with at least 3 doctoral candidates each. Lubusz, Subcarpathian, Podlaskie, Warmian-Masurian and West Pomeranian have no such entity.

These data point to significant regional differences in the structure and distribution of doctoral candidates in Poland.

Industries (by PKD code) represented by employers

Throughout the programme, the vast majority of entities were indicated by individual participants only, which made it impossible to build indicator 12 for editions 1–4. Because of differences in the form of entries, such as extra spaces, special characters or different address formats, entity names were standardised on the basis of registered office data and the NIP tax number, and in case of doubt the POL-on and GUS registers were consulted.

The table below shows the industries (by PKD code) represented by employers in editions 5–7.

An analysis of the data assigning employers to industries by main PKD codes reveals several dominant areas of activity. The largest cluster of companies and institutions is in IT and R&D, covering codes 62 (computer programming and IT consultancy) and 72 (scientific research and development). These industries include companies such as AIUT, ATOS and ALPHAMOON, as well as numerous institutes of the Łukasiewicz Research Network.

Another important area is the electrical engineering and electronics industry, dominated by codes 26, 27 and 28, covering the manufacture of electronic equipment, machinery and parts. Representatives of this industry include APTIV SERVICES POLAND, ATMAT and PIT-RADWAR. A large number of entities also operate in the chemical and pharmaceutical industry, codes 20 and 21 — including GRUPA AZOTY, CELON PHARMA, HENKEL and ORLEN. Other prominent sectors include: – Energy (code 35), with companies such as ENERGA, PGNiG Termika, ORLEN and POLSKIE

GÓRNICTWO NAFTOWE I GAZOWNICTWO, – Górnictwo i przemysł surowcowy (kody 05, 06, 09) z podmiotami jak Jastrzębska Spółka Węglowa czy Chemkop, – Produkcja i inżynieria materiałowa (kody 24, 25, 33), z takimi firmami jak ArcelorMittal, H. Cegielski, Mesko.

The list also includes public institutions (e.g. the Ministry of Finance, Główny Urząd Miar, Urząd Dozoru Technicznego) as well as hospitals, universities and research institutes, often assigned to code 72.19.

In summary, the data show strong representation of modern, highly specialised industries — above all IT, research and development, energy, chemicals and electrical engineering. They form the technological, industrial and scientific foundation of the contemporary economy.

The list of PKD codes for edition 8 covers a very wide range of business activities, but its structure clearly shows the dominance of the industrial and technology sector, as in editions 5–7. The most strongly represented activities are scientific research and development, engineering and professional and technical activities, which shows the importance of R&D facilities, design and technical analysis for the entire economic ecosystem. At the same time, the IT and digital services sector is very prominent, covering software development, IT consultancy, data processing and telecommunications, which provide key technological support for other industries. A significant part of the set is also manufacturing, in particular chemicals, pharmaceuticals, metals, machinery, electrical equipment and medical and electronic equipment, which indicates

a predominance of medium- and high-technology production. This structure is complemented by extensive construction and infrastructure activity, carrying out building, installation and engineering projects, as well as wholesale and retail trade, which mainly plays a supporting role for industry and services. The energy, raw materials, water, wastewater and waste management sectors are also present, as are transport and logistics, which provide operational support for production and trade.

The picture is completed by services in healthcare, education, administration and social activities, which are complementary in nature. As a result, the PKD set analysed presents a coherent picture of a diversified economic ecosystem that is clearly oriented towards innovation, industry and technology, with R&D, IT and production at its core.

The list of PKD codes for entities from editions 5–8, sorted in ascending order by PKD code, is presented below.

The largest group comprises activities related to scientific research, development, engineering and technical activities, covering above all PKD codes 72 (R&D), 71 (engineering and architectural activities, technical testing and analysis) and 74 (other professional, scientific and technical activities). This area clearly dominates the entire list.

• –72.19 – Other research and experimental development on natural sciences and engineering – 99
• –71.20 – Technical testing and analysis – 39
• –74.90 – Other professional, scientific and technical activities – 34
• –71.12 – Engineering activities and related technical consultancy – 33
• –72.11 – Research and experimental development on biotechnology – 23
• –62.01 – Computer programming activities – 20
• –26.51 – Manufacture of instruments and appliances for measuring, testing and navigation – 14
• –85.59 – Other education – 13
• –74.10 – Specialised design activities – 12
• –70.22 – Business and other management consultancy activities – 11

The results clearly confirm that R&D and engineering and technical activities are the largest and dominant group, with PKD 72.19 as the clear leader. However, very diverse industries also appear, including some seemingly far removed from a doctorate, such as photographic activities. This shows a willingness to conduct research practically regardless of the area.

Geographical data for employers (by registered office)

The study divides implementation doctorates by city size. They are divided into 10 groups, shown below.

Chart 4.32Number of implementation doctorates by town size, editions 5–8

Over 1 million

198

500,000 to 999,999

190

100,000 to 249,999

159

250,000 to 499,999

105

20,000 to 49,999

68

50,000 to 99,999

60

10,000 to 19,999

58

Under 2,000

48

2,000 to 4,999

43

5,000 to 9,999

23

An analysis of the localities in which the registered offices of companies running implementation doctorates are located shows a strong concentration of these entities in the largest urban centres. The most doctoral candidates were recorded in cities with more than 1 million inhabitants (198) and in the 500,000–999,999 range (190), which clearly shows that the largest agglomerations are the main centres for implementation doctorates. Medium-sized cities also have high numbers — 100,000–249,999 inhabitants (159) and 250,000–499,999 (105) — which shows the important role of these centres as a base for science–business cooperation. As town size decreases, there is a clear drop in the number of companies running implementation doctorates. In towns with 50,000–99,999 inhabitants (60) and 20,000–49,999 (68), the share is clearly smaller, though still noticeable. The fewest implementation doctorates are in small towns and villages — 10,000–19,999 inhabitants (58), 5,000–9,999 (23), 2,000–4,999 (43) and under 2,000 (48).

The distribution as a whole confirms that implementation doctorates are above all the domain of large and very large cities, where there are more companies, well-developed R&D facilities and better conditions for cooperation with universities. At the same time, the presence of companies running implementation doctorates in smaller towns and villages shows that doctorates can be carried out outside large agglomerations, although their scale there remains clearly limited. This is also related to the overall potential of cooperating entities in less densely populated areas and their much lower concentration.

Analysis and summary of the statistical results

The statistical analysis makes it possible to look at the Implementation Doctorate Programme from the perspective of its first eight editions, covering 2017–2024. The data collected show both the scale of the undertaking and its clearly defined scientific, institutional, economic and territorial profile. The programme is neither marginal nor limited to individual universities or companies. In the period analysed, a total of 3,052 participants qualified, representing numerous research institutions, companies and regions of the country. At the same time, the results show significant concentrations — above all around technical universities, engineering and technical sciences, large academic and industrial centres, and companies conducting R&D and technological activities.

Analysing the dynamics of the programme’s development, one can see that the number of projects accepted in successive editions varied considerably. The first edition, in 2017, covered 385 projects; in the next the number rose to 461, and in 2019 it fell to 364. The programme reached its largest scale in the fourth edition, in 2020, when 588 projects were accepted. In the following two years the number of participants remained high — 476 and 440 projects respectively — while in 2023 there was a sharp drop to 167 accepted projects. In 2024 the result was similar, at 171.

This means that the programme’s development can be divided into several stages. The first covers the period of building interest and growing scale, ending with the record edition of 2020. The second is a period of relatively high stability in 2021–2022. The third begins in 2023 and is characterised by a clearly lower number of newly funded projects. This decline should not, however, automatically be interpreted as a decrease in interest in the idea of the implementation doctorate itself.

The study indicates that an important factor was the setting of a total amount of funds for new projects, which raised the score threshold for obtaining funding. The number of accepted projects is therefore not only a measure of interest in the programme but also a result of funding policy and the conditions of successive calls. With some caution, it can be said that this limitation raised the standard of the projects awarded, which should also be followed by fewer withdrawals and problems with reports. The total of 3,052 accepted projects nevertheless shows that the programme has reached a significant scale. After eight editions, the implementation doctorate can be regarded as an established element of the system of cooperation between science and the socio-economic environment rather than an experimental solution.

The programme’s concentration on engineering issues and the dominant position of technical universities are also noticeable. The largest number of participants was at the Silesian University of Technology — 470. Next come AGH University of Krakow — 387 participants — and Warsaw University of Technology — 372. They are followed by Wrocław University of Science and Technology, Gdańsk University of Technology, Poznan University of Technology, Cracow University of Technology and Lodz University of Technology. Classic, economics and medical universities appear only further down the list. This does not mean that the programme is closed to them. On the contrary, the presence of universities, medical, life sciences and economics universities, non-public universities and even art academies and institutes of the Polish Academy of Sciences shows its interdisciplinary nature. The scale of participation of these entities is, however, much smaller than that of the largest technical universities. This structure is consistent with the very concept of the implementation doctorate. Projects are meant to respond to real technological, organisational and economic problems, so the programme’s natural base is universities with strong links to industry, extensive laboratory infrastructure and experience in R&D for companies. Technical universities also have extensive networks of cooperation with industry, which makes it easier to create the doctoral candidate–university–employer arrangement. At the same time, such a strong concentration of the programme in the largest technical universities may be a pointer for its further development. There is room to increase the participation of other types of higher education institutions and to popularise implementation doctorates in areas where the implementation potential may be less obvious than in the technical sciences but is still significant.

Another element is strong regional differentiation. The structure of the universities taking part in the programme translates directly into its territorial distribution. The largest number of participants was assigned to the Silesian Voivodeship — 605 — followed by Lesser Poland — 574 — and Masovian — 563. The Lower Silesian (360) and Greater Poland (257) voivodeships also reached a significant scale. At the other extreme are the Lubusz, Warmian-Masurian, Świętokrzyskie and Podlaskie voivodeships, where the number of participants was many times lower. This distribution points to a clear concentration of the programme in regions that have both large academic centres and a developed industrial and economic base. Silesia has the Silesian University of Technology and an extensive base of industrial companies, Lesser Poland has AGH and Kraków’s strong academic community, and Masovia has Warsaw University of Technology, the University of Warsaw and the largest concentration of companies and institutions in the country. So it is not only the size of the university that matters. The programme develops most intensively where several favourable factors occur at once: scientific potential, the presence of companies conducting R&D, adequate infrastructure, experience of science–business cooperation and the activity of local staff and university administration. Lower interest in some voivodeships may result from fewer large universities, a weaker industrial base, the structure of the local economy, less intensive promotion of the programme and less developed cooperation networks. The regional analysis therefore points to the possibility of a more differentiated information policy. If one of the goals of the programme’s further development were to increase its territorial accessibility, stronger support for universities and companies from regions so far less represented would be justified. The structure of participants by gender is also interesting. Men predominate in all eight editions. Of the 3,052 participants, 2,167 were men and 878 women; in seven cases gender could not be determined from the available data.

This means a ratio of approximately 70% to 30%. The predominance of men persisted in all editions, although the share of women was not constant. In the first edition there were 115 women and 270 men, while in the record year 2020 the figures were 156 and 429 respectively. In the sixth edition the share of women was relatively higher — 148 women compared with 292 men. In the last two editions the size of both groups fell, as a consequence of the reduction in the total number of funded projects. The gender structure is closely linked to the programme’s scientific profile. The largest group of doctoral candidates carry out projects in engineering and technical sciences, where the share of men is traditionally much higher. The situation is more balanced in natural and exact sciences, among others, while in medical and health sciences women form a more significant — and in some tables even larger — group. The data therefore indicate that gender differences in the programme should not be analysed in isolation from the structure of disciplines. At the same time, the predominance of men persisting across all editions may justify further analysis, particularly in the fields that form the core of the programme.

The dominance of engineering and technical sciences was already evident in the analysis of universities. The most characteristic feature of the programme is its clearly engineering and technical profile. After taking into account changes in the classification of fields and disciplines over successive editions, the largest part of the participants falls precisely within this area. Natural and exact sciences, social sciences, medical and health sciences, agricultural sciences, the humanities, veterinary sciences and the arts are much less represented. A detailed analysis of disciplines further confirms this direction. Among technical disciplines, mechanical engineering is particularly well represented, with 343 participants. Next come materials engineering — 290 — and environmental engineering, mining and energy — 229. A large number of projects was also recorded in information and communication technology and in automation, electronics and electrical engineering. Chemical engineering, chemical technology, machine construction and operation, civil engineering and transport, and power engineering also have significant shares.

This distribution reflects the practical nature of the programme very well. These are areas particularly closely linked to industry, production, energy, digitalisation and modern infrastructure. In these fields it is relatively easy to identify a specific technological problem and a potential implementation result, and companies have both the facilities to conduct research and the possibility of using its results in practice. This does not, however, mean that the programme is closed to other areas. The tables include the social, economic, medical, pharmaceutical, agricultural sciences and humanities, as well as the arts. This shows that an implementation doctorate can be carried out wherever a real recipient of the results and a practical problem to solve can be identified. The scale of these projects is, however, still many times smaller than in the technical sciences.

Let us now summarise the subject matter of the projects. Particularly valuable information is provided by the analysis of more than 15,000 keywords assigned to the projects. It shows not only the formal assignment of projects to disciplines but also the actual subject matter of the problems the doctoral candidates deal with. By far the most frequent terms were “artificial intelligence” and “machine learning” — 132 and 131 occurrences respectively. The third most frequent term was “optimisation” — 90 occurrences. These three terms alone show one of the main directions of the programme’s development: the use of advanced digital methods to solve specific economic and technological problems. The next group consists of terms related to modern industry and technological transformation: neural networks, modelling, data science, 3D printing, automation, Industry 4.0, data analysis, diagnostics and natural language processing. This indicates a strong presence of issues related to digitalisation and process automation. The second clear stream is energy and environmental issues. Frequent terms include recycling, energy, sustainable development, renewable energy sources, energy efficiency and hydrogen. This shows that the programme also responds to the energy transition and the growing importance of the circular economy and environmental technologies. The third stream covers classic materials and industrial issues: corrosion, composites, microstructure, polymers, rail vehicles and issues related to production processes.

This means that new digital areas are not displacing traditional engineering topics but are increasingly integrated with them. The keyword analysis thus shows that implementation doctorates focus largely on issues at the centre of contemporary economic change: digitalisation, automation, artificial intelligence, energy, new materials and sustainable development.

It is also worth looking at the cooperating entities and, more generally, at the importance of companies. The analysis of employers confirms the programme’s broad economic reach. A characteristic feature is the high dispersion of entities — the vast majority appear in the database in connection with individual participants.

This means that the programme is not limited to a small group of the largest companies but is used by a wide range of organisations. At the same time, there is a group of companies and institutions that repeatedly engage in implementation doctorates.

The list includes entities from the automotive, energy, machine-building and metal, chemical and pharmaceutical, information technology, transport, R&D and public sectors. The recurring participation of some organisations may indicate that, after gaining their first experience, companies see the value of the programme and decide to launch further projects. From the perspective of the programme’s further development, it may be particularly important to use the experience of these entities as a source of good practice. Organisations running several doctorates have practical knowledge of choosing topics, cooperating with the university, the role of the assistant supervisor, providing the doctoral candidate with conditions for research and the subsequent implementation of results.

The industry structure of employers’ activities also confirms the research and technological nature of the programme. In the available PKD data, activities related to scientific research and development in natural sciences and engineering clearly dominate. Technical testing and analysis, professional, scientific and technical activities, engineering activities and technical consultancy, biotechnology and software-related activities are also very clearly represented.

There is also a significant presence of companies manufacturing equipment and apparatus, and in design, energy, chemicals, pharmaceuticals, metals and transport. The database also includes industries that are less obvious from the perspective of the traditional understanding of the implementation doctorate. This is an important signal, as it shows that implementation potential is not limited to classic heavy industry, technology or IT. The industry data should, however, be interpreted with caution, as a single employer may have many PKD codes, and the available information does not always make it possible to identify which of them is its main area of activity. Nevertheless, the overall picture is clear: the programme is particularly closely linked to entities for which R&D, engineering and technological activity is an important part of their operations.

Geographically, as with universities, the distribution of employers also points to a strong concentration in the largest economic regions. In the analysis of entities in which at least three doctorates were carried out in editions 5–7, the Masovian Voivodeship clearly dominates. There, 154 doctorates were carried out in such companies, compared with 75 in the Silesian and 55 in the Lesser Poland voivodeships. Greater Poland came next. In terms of the number of entities running at least three doctorates, Masovia also dominated, followed by the Silesian and Lesser Poland voivodeships. Some regions did not have a single employer meeting this criterion. It should be stressed, however, that the programme includes a very large number of organisations running a single doctorate, so the absence of a company with at least three doctoral candidates does not mean that a region is inactive. The results do, however, show where organisations have emerged that use the programme in a more systematic and repeated way. The concentration of both universities and the most active employers in similar regions suggests the existence of local ecosystems favourable to implementation doctorates. Where a strong technical university, companies with R&D facilities and experienced researchers and industry specialists operate close to one another, it is easier to initiate and carry out further projects.

When interpreting the results, several important methodological limitations should be taken into account. Data for the first seven editions were provided in a single structure, while information for the eighth edition came from a separate data set prepared in a slightly different way. For this reason, not all indicators could be calculated uniformly for the entire period. A further problem is the changes in the classification of fields and disciplines of science. In the period analysed, both the names and the assignment of some disciplines changed, so simple comparisons between the first and later editions may lead to erroneous conclusions. Applicants could also indicate more than one discipline, which means that the totals in some tables are not equivalent to the number of unique participants. There are also limitations in the data on cooperating entities. Because of the way data were collected and the need to protect personal data, some analyses of employers were possible only for later editions. Many tables also include only entities associated with at least three participants. The rankings of employers presented should therefore not be treated as a complete picture of all companies taking part in the programme. Company names required additional normalisation because of differences in how they were recorded, and PKD classification is complicated by the fact that a single entity may have many activity codes without a clear indication of the dominant one. These limitations do not undermine the value of the analysis, but they indicate that individual indicators should be interpreted above all as a description of the structure and main trends, and not as a basis for very precise comparisons of every edition and every category.

The overall picture emerging from the statistical analysis is coherent and allows several fundamental conclusions to be drawn. First, the Implementation Doctorate Programme has reached a significant scale, covering more than three thousand accepted projects in eight editions. Despite a considerable reduction in the number of new projects in the last two editions analysed, the programme remains a recognisable and established instrument of cooperation between science and the economy. Second, the programme has a clearly technical and industrial core. The largest group of participants are from engineering and technical sciences, and technical universities dominate among the institutions running the doctorates. Areas such as mechanical, materials and environmental engineering, mining and energy, automation, electronics and information and communication technology are particularly strongly represented. Third, the subject matter of the doctorates shows that the programme responds to the current needs of the economy. The dominance of artificial intelligence, machine learning and optimisation, together with the strong presence of energy, automation, materials technologies, recycling and sustainable development, shows its close link to the digital, technological and energy transitions. Fourth, there is significant spatial differentiation. The programme develops above all in regions with strong academic and industrial centres. Silesia, Lesser Poland, Masovia, Lower Silesia and Greater Poland account for a large part of university activity, while the most active employers are concentrated mainly in Masovia, Silesia and Lesser Poland. This may indicate regional science–business cooperation ecosystems, but it also shows room for greater inclusion of less represented regions. Fifth, the gender structure remains uneven. Women make up about 30% of participants, which is largely related to the dominance of technical fields. At the same time, the differences between individual areas of science show that this proportion is not uniform and requires a more in-depth interpretation. Sixth, the programme is not based solely on a few large companies. On the contrary, a large number of employers appear in the database with only a single doctoral candidate. This shows the programme’s wide reach and its ability to engage very different organisations. At the same time, there is a group of entities using the programme repeatedly, which may indicate that a lasting mechanism of cooperation with universities has developed. The results therefore show a programme that is mature in scale and has a clearly defined identity. Its greatest strength is its ability to combine the scientific potential of universities with the specific problems of companies and institutions. The statistics confirm that implementation doctorates are concentrated where there is a real need for research leading to practical results. At the same time, the programme’s further development may take into account the visible disproportions. This concerns above all the uneven participation of regions, the relatively small presence of some fields of science and the lower participation of women. This does not mean that the technical character of the programme, which follows from its natural function and the needs of the economy, should be weakened. It seems more appropriate to broaden its reach by promoting good practice, activating new universities and employers and showing implementation opportunities also in those fields and regions where they have so far been used to a lesser extent.

It should be noted, however, that these disproportions are fairly natural and result from objective conditions, which makes any attempt to reduce them considerably more difficult. The analysis also points to the need for further systematic collection and standardisation of data. Standardising the way information on employers, disciplines, location, industries and project results is recorded would make it possible in future to analyse changes between editions much more accurately. It would be particularly valuable to link input data on participants and projects with information on their later results: completion of the doctorate, type of implementation, the graduate’s further career and continued cooperation between the university and the company.

In summary, on the basis of the data to date, the Implementation Doctorate Programme can be regarded as a lasting element of the system supporting knowledge transfer between science and the economy. Its profile meets the needs of the contemporary technological economy, and the broad base of participating institutions shows its ability to function in different organisational environments. Further monitoring of the programme should focus not only on the number of new participants but above all on the quality, durability and effects of the projects — in particular on actual implementations, the development of competences, the impact on companies’ innovativeness and the emergence of lasting relationships between science and business.