Thesis in Development of a Learning Based Compositional Electrical Drive Model
Thesis in Development of a Learning Based Compositional Electrical Drive Model
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Job Description
The identification of accurate simulation models of electric drive systems comprising the inverter an electric driver and further components is a crucial step for the design of high-performing controllers fault diagnosis and many other tasks. Goal of the thesis is to develop a compositional model for electric drives that allows for simulation identification and control using automatic differentiation techniques. The main idea is to implement differentiable models for components of an electric drive that can be freely combined to an overall system model.
- You will familiarize yourself with physical models of electric drives (electric machines inverters ).
- You will do literature research on existing (ML-based) approaches for the identification of electric drives.
- Furthermore you will develop the dynamical physical electrical drive model combined with data-based models.
- Last but not least you will implement the proof of concept to demonstrate the gradient-based optimization of the overall model for a given example system under using dynamical data with ODE solvers.
Qualifications :
- Education: studies in the field of Electrical Engineering Cybernetics Physics Computer Science or comparable
- Experience and Knowledge: in Machine Learning and Python; modelling of dynamical Systems
- Personality and Working Practice: you are flexible enthusiastic and responsible
- Languages: good in German and 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
David Gnzle (Functional Department)
49 0
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
