Doctoral Position „Implementation of Virtual Sensors Using Physics-Informed AI Methods“

PROMOTIONAL ENVIRONMENT AND TOPIC

Within the research group “Application of AI-based virtual sensors for effective precision enhancement of positioning systems – VirtuSen”, we will collaborate on quantifying the effects of environmental disturbances on high-precision positioning and measurement systems in order to reduce their impact on the positioning performance. Typical examples of these environmental disturbances are variations in air temperature, pressure and humidity. It is of special importance to also know the spatial distribution of these parameters. The extensive determination of these is usually technically not realizable, since an unreasonable number of measurement points would be required. In practice, the common approach is to determine the disturbances only at few positions through a limited number of sensors.

AI methods can be used to map complex system behaviour based on a small number of input variables. This approach will be used in VirtuSen to enable the evaluation of the spatial distribution based on fewer input values from real sensors. The aim is to use additional AI-based virtual sensors for the disturbance variables to predict the behaviour of the overall system so that it can be specifically influenced.
This task is to be processed collaboratively by three PhD candidates, with the mechatronics and system design team ensuring extensive and holistic academic and professional support.

The subproject and doctoral thesis "Implementation of Virtual Sensors Using Physics-Informed AI Methods" contains:

• The selection of solution options for the implementation of virtual sensors using physics-informed AI approaches
• Conducting studies to evaluate the models regarding the suitability of the methods as well as potential limitations concerning the implementation and positioning of the virtual measurement points or the resulting behavior prediction
• The estimation of confidence in AI-generated results
• The evaluation of the methods and an exchange on new requirements for physical modeling
• The scientific documentation and publication of the results, including participation in relevant conferences

As an application-orientated research institute, IMMS works on innovative solutions and products, transfers the latest scientific results to industry and supports small and medium-sized enterprises in particular. The research topics are in the areas of integrated sensor systems, smart distributed measurement and test systems and magnetic 6D direct drives with nm precision.

We are looking for people who are enthusiastic about applying AI algorithms to problems in real systems. This requires a profound grounding in mathematics / linear algebra and a good knowledge of an appropriate programming language (ideally Python) are important. These points should be proven on the basis of academic achievements or own projects:

• Machine learning and deep learning (theoretical and practical application
• AI-based prediction of sensor values or time series data
• Experience with physically-informed AI approaches, embedded systems, hardware-related programming (C/C++) and mechatronics is an advantage.

Other qualities required for successful research in this field are:

• Engineering degree (diploma, master's degree) in the field of computer science, data science, electrical engineering or related fields with an excellent degree
• a systematic and structured approach to problems and challenges
• the ability to explain complex relationships and ideas clearly and effectively
• Communication skills, ability to work in a team and organisational skills
• Clear verbal and written style and strong English language skills

• An attractive workplace in a modern, very well-equipped and industry-oriented research institute
• A full-time position with full focus on the doctoral project
• the realisation and supervision of the scientific work at IMMS (the examination is carried out via the connection to a German university of applied sciences/university and is subject to the respective state university law)
• Work directly at the interface between university and industry
• Work in a flexible and creative team and on innovative and challenging topics
• Salary based on the framework conditions of the public service of the federal states
• Professional qualification and attractive further training opportunities