Master Thesis Physically Informed Machine Learning Based System Identification in MEMS Gyroscopes
Summary
Develop ML models to identify MEMS gyroscope systems using real-world sensor data and physics-informed techniques in Python/PyTorch.
Are you eager to connect physical insights with advanced AI? We are offering an exciting master's thesis opportunity to work on physically informed machine learning for system identification and performance modeling of MEMS gyroscopes.
- During your assignment, you will construct machine learning (ML) algorithms for system identification and performance prediction of MEMS gyroscopes.
- You will examine MEMS gyroscope data through detailed analysis.
- Furthermore, you will assess physically informed ML in comparison to other architectures.
- Moreover, you will acquire a deep physical understanding of MEMS gyroscopes to optimize your models.
- Finally, you will work with real-world sensor data to validate your findings.
- Education: Master studies in the field of Informatics, Physics, Engineering or comparable with good grades
- Experience and Knowledge: in data driven parameter identification; knowledge of Python, PyTorch, Pandas, and Probabilistic Modeling; practical experience with hands-on data handling and pipeline construction
- Personality and Working Practice: you excel at structuring your tasks systematically, actively exploring new concepts with curiosity, and driving results with high motivation
- Work Routine: your on-site presence is required
- Languages: fluent in English and good in German
Start: according to prior agreement
Duration: 6 months
It is possible to include a 1-month internship before starting the thesis
Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, job references 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 Ullmann (Functional Department)
+49 7121 354652
Max Laser (Functional Department)
+49 173 2520609
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