Master Thesis Meta-learning for Fast Identification of Models of Electric Machines Robert-Bosch-Campus 1, 71272 Renningen, Germany Full-time Robert Bosch GmbH Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! Job Description The identification of accurate simulation models of electric machines is a crucial step for the design of high-performing controllers, fault diagnosis, and many other tasks. Often, the time available for performing measurements on a physical device is limited. One general approach to still obtain an accurate model under such constraints is transfer- and meta-learning. We further aim to combine this with knowledge on the physics of the electric machine by forming a grey-box model, which promises to further reduce the required measurement time. The goal of this thesis is to investigate grey-box meta-learning approaches for accurate identification of models of electric machines, given tight measurement time constraints. During your thesis you will conduct comprehensive literature research on meta-learning and transfer-learning approaches. You will familiarize yourself with physical models of electric machines. Furthermore, you will design and implement meta-learning models, as well as train algorithms for the identification of electric machines. Finally, you will evaluate and benchmark the developed approaches through simulation. Qualifications Education: studies in the field of Mathematics, Physics, Electrical Engineering, Cybernetics, Computer Science or comparable Experience and Knowledge: in modelling of dynamical systems, machine learning and Python Personality and Working Practice: you excel at working independently and systematically organizing your tasks Languages: very good in English Additional Information Start: according to prior agreement Duration: 6 months Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Jan Achterhold (Functional Department) Jan.Achterhold@de.bosch.com LI-DNI