Master Thesis Robust Identification of Compositional Electrical Drive Models - Identifiability, Sensitivity & Excitation Analysis
Core
Develop robustness diagnostics for a physics-based electric drive identification pipeline, focusing on parameter identifiability, sensitivity, and excitation analysis.
Role type
Master Thesis Researcher (System Identification & Control)
Builds
Robust identification pipeline diagnostics and countermeasures for electric drive models
Domain
Automotive engineering, electrical drives, system identification
Deliverable
production ML models | research
Required skills
Python programming, dynamic systems modeling, differential equations, physics of electrical machines, experimental design
Preferred skills
MATLAB/Simulink, machine learning, automatic differentiation (JAX/PyTorch), handling partially observable states
Technologies
Python, MATLAB/Simulink, JAX, PyTorch
Responsibilities
Review literature on robust system identification, develop diagnostics for identification pipelines, analyze excitation content of datasets, evaluate approaches using benchmark use cases, document and present research findings
Seniority
Master's level research project