Master Thesis Data-Driven Modeling of Inverse Lateral Motorcycle Dynamics

Robert Bosch Group; Renningen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A leading technology company in Renningen is offering a Master thesis opportunity focused on data-driven modeling of inverse lateral motorcycle dynamics. Candidates should have a strong academic background in Computer Science or related fields, programming skills in Python, and experience with deep learning frameworks. The role involves evaluating machine learning methods and collaborating with an interdisciplinary team to enhance motorcycle safety systems.

Qualifikationen

• Strong academic record in relevant field.
• Basic understanding of systems theory and vehicle dynamics is an advantage.
• Team player with a passion for innovation.

Aufgaben

• Evaluate classical and deep learning-based methods for time-series prediction.
• Analyze motorcycle dynamics data.
• Identify suitable modeling approaches and assess results.

Kenntnisse

Programming skills in Python

Hands-on experience from relevant projects

Good in German or English

Ausbildung

Master studies in Computer Science, Engineering, Natural Sciences or comparable

Tools

PyTorch

TensorFlow

Jobbeschreibung

Master Thesis Data-Driven Modeling of Inverse Lateral Motorcycle Dynamics

Full-time

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbHis looking forward to your application!

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.

• 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

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?
Alexander Lutzke (Functional Department)
[phone]