Doktorand inom elektroteknik med fokus på tillförlitlighet i elmotorer
Core
Developing automated test benches and predictive lifetime models for electric motor reliability under high dv/dt stress from SiC/GaN power electronics.
Role type
PhD researcher (PhD student) in electrical engineering
Builds
Predictive lifetime models and accelerated aging test data for motor insulation and bearings
Domain
Electric power engineering / Power electronics / Electric motors
Deliverable
production ML models | research
Required skills
Statistical analysis, Signal processing, Machine learning, Laboratory work in power electronics, Programming (MATLAB/Python), Knowledge of motor insulation systems or mechanical bearings
Preferred skills
Experience with high-frequency measurements, Experience with motor test benches
Technologies
MATLAB, Python, SiC, GaN
Responsibilities
Develop automated test benches for controlled electrothermal aging, Perform accelerated aging tests on insulation materials and bearing units, Analyze measurement data and develop predictive lifetime models, Validate models on system level with motor test benches
Seniority
PhD candidate (early career researcher)
