Are you driven to improve vehicle safety and performance? We are looking for a talented master’s student to develop a state-of-the-art control system for vehicle stability, a critical component for the future of driving and autonomous systems. The goal of this thesis is to design and validate an innovative control function that enhances vehicle stability and handling beyond today's standards.

• During your thesis you will work on a real-world automotive application, contributing directly to the development of safer and more agile vehicles.

• You will develop a robust control strategy to maximize vehicle stability and performance under challenging conditions.

• Furthermore, you will investigate and apply advanced control methodologies. Possible approaches include data-driven techniques like Reinforcement Learning (RL) or established methods such as Model Predictive Control (MPC).

• In a professional simulation environment, you will implement and rigorously test your solution.

• Finally, you will analyze and present your findings, contributing to a key area of automotive innovation.

• Education: Master studies in the field of Cybernetics, Engineering, Mathematics, Computer Science or comparable with good grades

• Experience and Knowledge: profound knowledge of machine learning and control engineering; experience in Python DL frameworks like Pytorch, Tensorflow, or Jax

• Personality and Working Practice: you excel at combining an autonomous, systematic working practice with sharp analytical thinking

• Languages: very good in English

Start: according to prior agreement

Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?

Felix Berkel (Functional Department)

+49 711 811 92301

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