Master Thesis Robust Identification of Compositional Electrical Drive Models

Job Description

• During your thesis, you delve into physics-based models of electric drives and our Python component library while simultaneously reviewing current literature on robust system identification and the design of experiments.
• Furthermore, you develop innovative robustness diagnostics for our existing identification pipeline - considering parameter identifiability, sensitivity, and convergence behavior - and implement as well as quantify appropriate countermeasures.
• In Addition, you analyze the excitation content of given datasets, evaluate relevant criteria, and link these insights to the achievable identification quality.
• You comprehensively evaluate the approach you have developed using a practical benchmark use case.
• Moreover, you document as well as present your research findings clearly and understandably.
• Lastly, you extend the approach to address partially observable effects resulting from states that cannot be measured directly, such as temperatures.

Qualifications

• Education: Master studies in the field of in Cybernetics, Engineering, Mathematics, Computer Science, or comparable
• Experience and Knowledge: basic knowledge of dynamic systems and differential equations; proficiency in Python programming; understanding of the physics of electrical machines; optional: experience with MATLAB/Simulink for interacting with legacy simulation models; optional: knowledge of machine learning and automatic differentiation frameworks (JAX or PyTorch)
• Personality and Working Practice: you work independently and systematically on complex issues and possess strong analytical skills, enabling you to accurately grasp and solve problems
• Work Routine: office attendance required
• 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?
Benjamin Hartmann (Functional Department)
+49 7062 911 7020

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