Master Thesis Physically Informed Machine Learning Based System Identification in MEMS Gyroscopes

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