Master Thesis Data-Driven Modeling of Inverse Lateral Motorcycle Dynamics

In the central research division of Robert Bosch GmbH in Renningen, you will be part of a team that is working on the motorcycle safety systems of tomorrow. Our shared goal is to reduce the risk of accidents for motorcyclists while maintaining high riding comfort and enjoyment. A key challenge is the precise modeling and estimation of inverse lateral motorcycle dynamics. This makes it possible to determine the necessary control inputs for a desired vehicle state. A new approach is to learn this dynamic using machine learning or deep learning.

• As a part of your Master thesis, you will evaluate classical and deep learning-based methods for time-series prediction.
• To this end, you will analyze motorcycle dynamics data.
• In addition, you will identify suitable modeling approaches, implement them in PyTorch and assess the results based on real datasets from test rides.
• We offer you the opportunity to collaborate in an interdisciplinary team with experts in deep learning and rider assistance systems. You will gain access to powerful GPU resources and extensive vehicle dynamics data, as well as engage in practically relevant research with direct application in safety-critical systems.

Do you have a Master's degree?, * Education: Master studies in the field of Computer Science, Engineering, Natural Sciences or comparable with a strong academic record

• Experience and Knowledge: hands-on experience from relevant projects; good programming skills in Python; experience with deep learning frameworks such as PyTorch or TensorFlow; a basic understanding of systems theory and vehicle dynamics is an advantage
• Personality and Working Practice: you are a team player with a passion for innovation and technology and an analytical and structured working style
• Languages: good in German or English

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