Masterarbeit: Robuste Identifikation zusammengesetzter elektrischer Antriebsmodelle — Identifizierbarkeit, Sensitivität & Anregungsanalyse (w/m/div.)
Core
Develop robustness diagnostics for an electrical drive identification pipeline by analyzing parameter identifiability, sensitivity, and excitation content of datasets.
Role type
Master's thesis researcher (control systems & data analysis)
Builds
Robustness diagnostics and countermeasures for an electrical drive identification pipeline
Domain
Automotive engineering, electrical drives, system identification
Deliverable
production ML models | research
Required skills
Python programming, dynamic systems, differential equations, physics of electrical machines, experimental design (Design of Experiments)
Preferred skills
MATLAB/Simulink, Machine Learning, automatic differentiation (JAX, PyTorch)
Technologies
Python, MATLAB, Simulink, JAX, PyTorch
Responsibilities
Review literature on robust system identification and experimental design; develop and implement robustness diagnostics; quantify countermeasures; analyze excitation content of existing datasets; evaluate the approach using a practical benchmark case; document and present research results; extend the approach to address partially observable effects like temperature.
Seniority
Master's student (6-month project)