Master Thesis Meta-learning for Fast Identification of Models of Electric Machines
Master Thesis Meta-learning for Fast Identification of Models of Electric Machines
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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)
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
