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 masters 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.

Qualifications :

• 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

Additional Information :

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)

Max Laser (Functional Department)

Work #LikeABosch starts here: Apply now!

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Remote Work :

No

Employment Type :

Full-time