Doctoral Student in Electrical Engineering: Motor Reliability
Investigating how fast-switching wide-bandgap power electronics impact the long-term reliability of electrical motors.
Best for:Best for graduate engineers with a background in power electronics, electrical drives, and data-driven modeling.
Overview
KTH Royal Institute of Technology invites applications for a doctoral student position within the Electrical Machines and Drives group. Funded by the TERMINATOR project, the research examines how fast-switching silicon carbide and gallium nitride power electronics create electro-thermal stresses that lead to stator insulation aging and bearing damage. The successful candidate will develop automated laboratory platforms, conduct accelerated aging tests, and build predictive lifetime models using statistical analysis and machine learning. This role involves close collaboration with major industrial partners including Traton, ABB, and ABB Robotics.
What you'll do
- Design and implement automated laboratory testing platforms for controlled electro-thermal aging experiments.
- Perform accelerated aging tests on motor stator insulation and bearings under specialized laboratory conditions.
- Process and analyze complex experimental datasets using statistics, signal processing, and machine learning techniques.
- Validate degradation and lifetime models on laboratory motor test benches under inverter excitation.
- Collaborate closely with industrial partners and actively disseminate research results through reports and presentations.
Requirements
Degree required: Master's degree or equivalent second-cycle qualification in electrical engineering or a related discipline
Degree offered: PhD
Language: English equivalent to English B/6 with strong written and verbal proficiency
Instruction: English
- Hands-on laboratory experience in power electronics, electric drives, or high-frequency measurements
- Proficiency in programming languages such as MATLAB or Python for automated control and data analysis
- Foundational understanding of stator insulation systems or mechanical bearings
- Demonstrated experience with statistical data analysis, signal processing, or machine learning
- Strong collaborative abilities and effective communication skills for industry cooperation
- Passed a second-cycle degree or completed course requirements of at least 240 higher education credits with 60 second-cycle credits
- Fulfilment of basic eligibility requirements for postgraduate education under the Swedish Higher Education Ordinance
Funding & terms
How to apply
Submit your application through the official KTH portal by following the online instructions. Ensure all required academic credentials, statements, and supporting documents are attached before the closing date.
- CV
- Master's degree certificate and transcripts
- Cover letter
- Reference letters
Contact
Luca Peretti· Associate Professor
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