Master Thesis on Reinforcement Learning-Based Control Concepts for Permanent Magnet Synchronous Machines
Job Description
- During your assignment, you will develop reinforcement learning-based tracking controller concepts for permanent magnet synchronous machines (PSMs) with offset-free tracking.
- You will evaluate the developed controller concepts in simulation in comparison to state-of-the-art controllers.
- Last but not least, you will benchmark the developed concepts at the test bench (optional).
Qualifications
- Education : studies in the field of Cybernetics, Computer Science, Engineering, Mathematics or comparable
- Experience and Knowledge : in machine learning and control theory; MATLAB / Simulink and Python, ideally in DL frameworks;
knowledge of electrical machines is a plus
- Personality and Working Practice : autonomous, systematic working practice and analytic thinking
- Languages : good in German or 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?
Felix Berkel (Functional Department)
49 711 811 92301
Maximilian Manderla (Functional Department)
49 711 811 7297
LI-DNI
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