Company Description

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 GmbH is looking forward to your application!

Job DescriptionDuring your thesis you will identify or create scenarios, set up evaluation pipelines, and define metrics for performance and safety.As learning approaches, you will implement and compare various techniques - be it Reinforcement Learning, Imitation Learning, or Adversarial Imitation Learning - within the established pipeline. In form of extensive simulation, you will run large-scale experiments to assess convergence, training stability, and hardware requirements. Furthermore, you will conduct a comparative analysis to track key metrics (collisions, route completion, computational efficiency) and benchmark against rule-based or learned baselines. Finally, you will report your findings by documenting insights, highlighting strengths and weaknesses of each approach, and compile the results in a final report or publication-ready manuscript.

QualificationsEducation: Master studies in the field of Computer Science, Electrical Engineering, Mechatronics, Mathematics or comparableExperience and Knowledge: in Python, PyTorch; knowledge of Deep Learning, NumPy, Reinforcement Learning, Inverse Reinforcement LearningPersonality and Working Practice: you have an analytical mindset with excellent communication skills and are self-driven plus eager to solve real-world challenges in automated drivingLanguages: fluent in English

Additional Information

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?
Jürgen Mathes (Functional Department)
+49 152 08887043

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