Master Thesis Physically Informed Machine Learning Based System Identification in MEMS Gyroscopes
Core
Develop physically informed machine learning algorithms for system identification and performance modeling of MEMS gyroscopes using real-world sensor data.
Role type
Master's thesis researcher (Physically Informed Machine Learning)
Builds
ML models for system identification and performance prediction of MEMS gyroscopes
Domain
MEMS sensors, System Identification, Machine Learning
Deliverable
production ML models
Required skills
Python, PyTorch, Pandas, Probabilistic Modeling, Data-driven parameter identification, Hands-on data handling
Preferred skills
Deep physical understanding of MEMS gyroscopes (via careerplan.io/jobs/744000152913528-master-thesis-physically-informed-machine-learning-based-system-identification-in-mems-gyr)
Responsibilities
Construct ML algorithms for system identification, Analyze MEMS gyroscope data, Assess physically informed ML vs other architectures, Optimize models based on physical insights, Validate findings with real-world sensor data
