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Student (m/f/d) for internship and/or thesis on eMotor simulation prediction using AI and machine learning

Internship Machine Learning Artificial Intelligence Software Engineering Data Warehouse

Job Description

Topic:
The aim of this master thesis is to investigate how modern machine learning and artificial intelligence technologies can be used to improve the efficiency and speed of electric motor analysis while ensuring technological progress. The focus is on identifying challenges and developing solutions, which are illustrated using a case study for a specific type of electric motor.
 
Your main responsibilities:
  • Literature analysis on electromagnetism, electric drives, heat transfer, optimization and AI (artificial intelligence).
  • Development of simulation models to calculate the performance and thermal behavior of the engine.
  • Definition and implementation of the machine learning procedure.
  • Verification of AI-generated results through standard optimizations.
Your profile:
  • Studies in engineering (mechanical engineering or electrical engineering)
  • Experience in simulation and calculation, ideally in the field of electrical machines, CFD or FEA.
  • Experience in Ansys MotorCAD, Maxwell, Fluent and OptiSlang is an advantage.
We offer:
  • Interesting work in a modern and international working environment
  • State-of-the-art tools and software
  • Flexible working hours (home office possible)
  • Attractive, above-average remuneration
Internal Use Only: Indirect Hourly
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About BorgWarner

Reliably delivering what’s needed today There are few challenges as important today as creating solutions that support a cleaner, more energy-efficient world. This requires a commitment to constantly improve the transportation of people and things. We, at BorgWarner, made that commitment decades ago and have since been creating technologies to improve efficiency, emissions and performance in all types of vehicles

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