onsite
PhD - Scalable and Efficient Reinforcement Learning for Physical AI - Robert Bosch GmbH
Software Engineer
PhD research position focused on developing scalable, efficient reinforcement learning algorithms for physical AI systems, leveraging Python, C++, and advanced simulation tools to bridge AI and robotics.
About the role
Key Responsibilities
- Design and implement novel reinforcement learning methods that scale to real‑world physical systems.
- Develop high‑performance simulation environments for training and evaluating AI agents.
- Integrate learning algorithms with robotic hardware prototypes and assess real‑time performance.
- Publish research findings in top conferences and journals and present results to interdisciplinary teams.
- Collaborate with experts in control, perception, and software engineering to ensure end‑to‑end system robustness.
Requirements
- Master’s degree (or equivalent) in Computer Science, Electrical Engineering, Robotics, or a related field with strong fundamentals in machine learning.
- Hands‑on experience with reinforcement learning frameworks (e.g., TensorFlow, PyTorch, RLlib) and proficiency in Python and C++.
- Demonstrated ability to work with simulation tools such as Gazebo, PyBullet, or MuJoCo.
- Strong analytical and problem‑solving skills, with a track record of research publications or project deliverables.
- Excellent written and verbal communication skills in English.
Skills
reinforcement learningpythoncmachine learning