PhD – Generator of Optimized Winding Schemes in Electrical Machines

About the role

You will develop a novel mathematical approach for comprehensive modeling of pin winding (hairpin) systems, laying the foundation for innovative electric machine designs. You will also formulate robust mathematical representations of complex real‑world boundary conditions and integrate your models into existing simulation tools to optimise winding schemes based on defined KPIs.

Key responsibilities

• Develop a novel mathematical approach for comprehensive modeling of pin winding (hairpin) systems to enable innovative electric machine designs.
• Research and formulate robust mathematical representations of complex real‑world boundary conditions, including symmetry, operating conditions, and manufacturing constraints.
• Analyze computational complexity, feasibility and runtime performance of proposed discrete optimization algorithms and integrate the models into existing simulation tools to evaluate and optimise winding schemes based on defined KPIs.

Required profile

• Excellent university degree (Master’s or equivalent) in Applied Mathematics, Industrial/Technomathematics, Computational Engineering, Computational Engineering Science or a comparable field.
• Experience in discrete optimization, electromagnetic field simulation and applying mathematical concepts to real‑world problems, preferably in electric machines.
• Strong analytical skills, openness to new ideas, clear communication and proactive teamwork.
• Fluent in English; German language skills or willingness to learn.

Required skills

• Discrete optimization techniques.
• Electromagnetic field simulation.
• Object‑oriented programming principles.
• MATLAB.
• Python.
• Version control systems (e.g., Git).
• Visual Studio.