onsite
PhD - Scalable and Efficient Reinforcement Learning Methods for Physical AI - Robert Bosch GmbH
Software Engineer
Pursue a PhD focused on developing scalable, high‑performance reinforcement‑learning algorithms for physical AI systems, leveraging Python, C++, and simulation tools to advance robotics and autonomous applications.
About the role
Key Responsibilities
- Design and implement novel reinforcement‑learning algorithms that scale to real‑world physical systems.
- Develop high‑performance simulation environments for training and evaluating AI agents.
- Integrate machine‑learning pipelines with robotic hardware and sensor suites.
- Publish research findings in top conferences and journals.
- Collaborate with interdisciplinary teams to translate research into prototype applications.
Requirements
- Master’s degree (or equivalent) in Computer Science, Electrical Engineering, Robotics, or a related field.
- Strong background in reinforcement learning, deep learning, and control theory.
- Proficiency in Python and C++ for algorithm development and system integration.
- Experience with simulation frameworks (e.g., Gazebo, PyBullet, MuJoCo) and robotics platforms.
- Excellent problem‑solving skills, scientific writing ability, and a track record of publications or project deliverables.
Skills
reinforcement learningpythoncmachine learning