HE-Personal: Programme "FH-Personal" BMBF Project at Esslingen University of Applied Sciences

The federal government and the states have set up their joint “FH-Personal” programme to recruit and develop professorial staff at 64 universities of applied sciences (HAW). Esslingen University is one of 13 such universities in Baden-Württemberg to have received a positive response in the first round of funding and has been awarded six million euros in total, which will go to the university and its collaborating partners in the project. Its aim in setting up the “HE-Personal” project is to attract particularly qualified researchers with practical experience to teach and research in 8 future-oriented key research areas.

HE-Personal in Detail

Main aims and starting points

A key aim of the project is the future appointment of highly qualified professors for the important teaching and research fields at Esslingen University. To realise this aim, the task of actively scouting for and encouraging women to apply for professorial positions and junior scientist posts will be professionalised and extended.

Esslingen University is thus part of a collaboration made up of local universities, a non-university research institute, industrial companies and civil society organisations. This is a novel feature of the project. It will ensure that the next generation of professors across different discipline-specific research areas has the academic qualifications, the professional experience outside the university setting, and the pedagogical suitability required.

Collaborative doctoral programmes with the traditional universities shall afford graduates of a university of applied sciences (HAW) the opportunity to embark on a career path leading to a professorship at a university of applied sciences (HAW professorship). Postdoc tandem posts at Esslingen University are intended to offer those with the necessary academic qualifications the opportunity to learn about the HAW professorships and at the same time continue to pursue their chosen non-university career. The postdoc tandem posts at traditional universities offered together with non-university partners serve to offer the researchers the opportunity to obtain qualifications for three potential career paths: a university professorship, a non-university career and a professorship at an HAW.

Measures to implement the goals

Esslingen University undertook a survey of all professors and part-time lecturers of the actual situation regarding appointment procedures in preparation for this programme. An analysis of the survey results and in-depth conversations with professors interested in research allowed Esslingen University to identify teaching and key research areas for the future. The project sets up collaborative doctoral programmes on the one hand and postdoc tandem posts on the other in these particular research areas, together with a special professorship at the helm.

Involving non-university partners in the postdoc tandem posts is a logical step because Baden-Württemberg’s Higher Education Act requires HAW professors to have had professional experience outside the university setting. The neighbouring universities of Stuttgart and Tübingen are incorporated into the project because they co-supervise the collaborative doctoral programmes and also because the project aims to open up the career path to an HAW professorship to their staff via postdoc tandem posts.

Furthermore, a professorial appointment manager (part of the Personnel Department) will professionalise the task of actively scouting for and encouraging women to apply for professorships. This work is being done in close collaboration with the Baden-Württemberg Conference of Equal Opportunities Representatives at Universities of Applied Sciences and the Baden-Württemberg Cooperative State University (LaKof BW), where numerous ideas for headhunting have already been put to the test and implemented.

Spotlight on Main Fields of Research

“Autonomous Systems” are one of the main applications of intelligent robotics. The application of machine learning for autonomous systems is one of the most active research fields in the world. The nature of the local industry means that this field is of particularly significant strategic importance for the Stuttgart region. Within the HE-Personal project, the “Autonomous Systems” research area is dedicated to research questions relating to artificial intelligence. The emphasis is on developing a methodology to reduce the resource consumption over the complete development process of AI systems and make their use more sustainable (“Green AI”), because training one single artificial neuronal network can currently incur costs amounting to millions.

The research topics in this area encompass various sub-fields, such as the automation of the architecture of AI systems to enhance efficiency; the learning capability of AI systems, especially from smaller quantities of data, to reduce the effort required for the (manual) data collection (self-supervised learning, weak supervised learning, few-shot learning, low-resource NLP); and the acquisition of a deeper understanding of AI systems to enable more targeted optimisation of the AI architecture (explainable AI).

The work on "Fuel Cells and Hydrogen Technologies" is undertaken in collaboration with research and industrial partners with the aim of further developing applications of the fuel cell systems in mobile and stationary systems. Topic-related lectures will be offered at Esslingen University to disseminate the results obtained. This research area addresses several subjects, including modelling and characterisation of fuel cell stacks; load spectrum determination as a real-world application; and the determination of ageing affects based on the load spectrum analyses. The research will investigate the design of suitable measures to increase the lifetime (incl. energy management concepts) of fuel cells, which will be implemented on a test rig or test vehicle to prove they bring about an improvement to the ageing behaviour.

The "Digital Twin" research area works with digital twins of industrial systems and immersive visualisation methods of mixed reality (e.g. augmented reality and virtual reality) and modern technologies for intuitive human-machine interaction (e.g. gesture recognition or voice control). The work in this research area deals with issues such as how to intuitively and immersively integrate multimodal human-model interactions in mixed reality environments using the example of industrial production, or how to reduce positioning errors when visualising the digital twin in a real environment in the context of mixed-reality-in-the-loop simulations—errors which arise through sampling and latency operations between industrial controls and mixed reality terminal devices.

Investigating these research issues creates the necessary basis for potential applications in machine and plant construction, for example for the immersive, virtual commissioning of control systems or staff training. The research results are continuously evaluated together with partners in industry and incorporated into teaching modules at Esslingen University.

This research area is designed to allow individuals with an entrepreneurial but responsible mindset to realise their potential in order for society to cope with the disruptive digital and environmental transformation of the economy and the socio-economic challenges of society.  They contribute to a dynamic and internationally competitive economy through innovative enterprises. The research area also supports opportunity-oriented start-ups, which utilise technical innovations to make an important contribution for the environment and society and create valuable jobs.

The research area "Entrepreneurship" thus deals with the following scientific topics in the areas of entrepreneurship and innovation:

  • Digital entrepreneurial process and innovation strategy
  • Sustainable entrepreneurial finance
  • Transdisciplinary entrepreneurial education for tech-entrepreneurs

This division includes both doctoral and postdoctoral research. The research results are developed and evaluated with industry partners; they add to the further scientific development of the Centre for Entrepreneurship at Esslingen University. With this applied research design, we increase the comparative competitive advantage of Esslingen University.

Innovations relating to nursing, medicine and technology are very important for the regional and national provision of healthcare. Providing people with healthcare is a complex undertaking: many chronically ill people use technical aids prescribed by nursing and medical staff, who evaluate their effect and provide advice. This means that technical aids are currently undergoing rapid development.

Esslingen University can play a special role when it comes to making headway with the scientific development and practical trials because it has the necessary key academic disciplines—management, nursing and technology—and close links with the respective fields of practical application. However, the academisation of the nursing sciences has only recently taken place, and the number of people for a professorial appointment is still very low. As a result, there is an urgent need to enable the necessary qualification of prospective professors.

Through HE-Personal, Esslingen University provides these prospective professors with the best possible training and in the faculty’s newest sub-fields, which are tackled through the “Interdisciplinary Collaboration between Nursing, Medicine and Technology" research area.


“IT Security” is a cutting-edge subject associated with all of the project research areas and is of crucial importance for the high-tech businesses in and around Stuttgart. The rapidly advancing digitalisation of business processes and the ever-increasing effects of digitalisation on our daily life in particular mean that both the potential danger for cyber attacks as well as the number of points of attack are continually increasing. At the same time, companies are confronted with regulatory stipulations which are rapidly becoming more demanding.

While the availability of IT security experts in the job market is so drastically low that graduates can expect to find extremely attractive positions in all branches in the future, currently hardly any people in the job market are qualified and suitable for teaching posts at universities of applied sciences. It is still difficult to recruit and retain professors in this field, leading to a shortage in teaching and hence in training as well. To counteract this shortage, one research area is concentrating on “IT Security”.


The "Power-to-X" research area will collaborate with research and industry partners to further develop applications for storing electricity generated from renewable resources in diverse energy carriers or raw materials. Topic-related lectures will be offered at Esslingen University to disseminate the results obtained. The research area addresses several aspects, including hydrogen generation by means of electrolysis and downstream plasma-induced methanation; optimisation of PV systems and wind turbines in combination with electrolysis plants; and high-pressure electrolysis to use the hydrogen in mobile applications. The participating researchers will investigate suitable business models with the aim of making the installations marketable, test them by implementing them in real energy and material systems, and validate the processes on test rigs they have developed themselves.

The main task of "Prognostics and Health Management (PHM)“ is to diagnose and prognosticate state of degradation of technical systems. The diagnosis and prognosis are often conducted using data-driven methods, which originate from the fields of statistics and machine learning. They are based on the statistical modelling of existing training data and therefore require a comprehensive amount of training data to be used in a purposeful way.

However, sufficient training data are seldom available for industrial PHM applications because generating data on the state of degradation of a technical system is usually very time-consuming and expensive. A possible solution—being investigated in this HE-Personal sub-project–consists of compensating for this lack of training data through the incorporation of process knowledge. This approach at finding a solution is an example of so-called theory-guided data science and involves incorporating various knowledge states. It begins by incorporating individual, physics-based laws into the training process and extends to the integration of detailed physical degradation models forming part of hybrid model ensembles.

Further information (in German)


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