PhD - Scalable and Efficient Reinforcement Learning Methods for Physical AI (f/m/div.)

At Bosch Research, we are pioneering the next generation of Physical AI-intelligent systems that can perceive, reason, and act robustly in the real world. From automated driving to robotics, our goal is to develop AI technologies that are not only highly capable but also safe, efficient, and scalable.

Despite remarkable progress in foundation models and end-to-end learning, today's autonomous systems still struggle to generalize reliably to new situations and often require enormous amounts of data and computational resources. For Bosch products, however, AI solutions must deliver strong performance while operating under real-world constraints such as limited compute, energy efficiency, safety, and reliability.

• In this PhD project, you will explore new approaches for creating the next generation of efficient autonomous decision-making systems.
• Your research will combine ideas from imitation learning, reinforcement learning, large-scale simulation, world models, and model compression to develop AI agents (across several embodiments) that can continuously improve, adapt to novel situations, and transfer effectively from simulation to reality.
• A key focus of the project will be enabling resource-efficient Physical AI by achieving the reliability and generalization of today's frontier AI systems while significantly reducing computational requirements for deployment on real devices.
• By joining Bosch Research, you will have the opportunity to work at the intersection of cutting-edge AI research and real-world industrial impact, collaborating with leading experts from academia and industry to shape future intelligent products and services.