Masterarbeit: Physikalisch informierte, maschinelle Lernverfahren zur Systemidentifikation in MEMS-Gyroskopen (w/m/div.)
Core
Develop physics-informed machine learning algorithms for system identification and performance modeling of MEMS gyroscopes using real sensor data.
Role type
Master's thesis researcher (physics-informed ML)
Builds
ML models for system identification and performance prediction of MEMS gyroscopes
Domain
Automotive sensors / MEMS technology / Machine Learning
Deliverable
production ML models
Required skills
data-driven parameter identification, Python, PyTorch, Pandas, probabilistic modeling, data pipeline creation
Preferred skills
deep physical understanding of MEMS gyroscopes
Technologies
Python, PyTorch, Pandas
Responsibilities
Develop ML algorithms for system identification, analyze MEMS gyroscope data, evaluate physics-informed ML against other architectures, validate results with real sensor data
Seniority
Master's student researcher
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