Your Mission:

At Porsche Motorsport, we continuously strive to improve the accuracy, robustness, and efficiency of so-called Digital Twins: digital representations of the system car-racetrack-driver. Within this thesis, you will focus on advancing our race driver model, which is based on reinforcement learning. The goal is to further develop the method and toolchain, targeting improved imitation quality, enhanced generalization, efficiency and performance. In this scope, there will be plenty of scientific challenges to overcome and there will be also opportunities to contribute to scientific publications.

Content of the Thesis:

• Development and implementation of methods to accelerate training of reinforcement learning driver models
• Investigation and application of domain randomization to improve robustness across varying conditions
• Tuning of hyperparameters and training schemes for enhanced model performance
• Evaluation of improvements within the context of lap simulation and setup sensitivity analysis
• Benchmarking against the current state of the art in simulation workflows

• Ongoing master studies in Computer Science, Data Science, Engineering, Mechatronics, or related fields
• Solid background in reinforcement learning and machine learning algorithms
• Practical experience with Python and ML frameworks (e.g. TensorFlow, PyTorch)
• Good programming skills in Python or similar
• Knowledge in vehicle dynamics and motorsport applications is an advantage
• Independent, structured, and solution-oriented working style
• Passion for motorsport and technology-driven performance optimization