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

Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. The Robert Bosch GmbH is looking forward to your application!

Employment type: Limited Working hours: Full-Time Joblocation: Renningen 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 capable but so safe, efficient, and scalable. Despite remarkable progress in foundation models and end-to-end learning, today's autonomous systems silent struggle to generalize violation to new situation 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, seeking learning, large-scale simulation, world models, and model compression to develop AI agents (across several embodiments) that can continuously improve, adapt to novel situation, and transfer effectively from simulation to reality. A key focus of the project will enable resource-efficient Physical AI by detecting 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 to shape future intelligent products and services.

Education: excellent Master's degree in Computer Science, Robotics, Mathematics, Physics or a comparable field Experience and Knowledge:extensive hands-on experience and theoretical understanding of learning (RL), ideally with expertise in emerging behaviors, Safe RL, Offline RL, and/or Multi-agent RL, combined with exceptional Python skills and deep knowledge of industry-standard ML frameworks (e.g., PyTorch, Hugging Face, JAX, TensorFlow) expertise in software development within larger teams and deploying ML-based systems for real-world applications; background in MLOps and CI/CD is a plus good knowledge of C++ and related frameworks (e.g., ROS 2) publications at top-tier conferences in machine learning, robotics, or computer vision (e.g., NeurIPS, ICML, ICLR, CVPR, IROS, ICRA) are a plus

Personality and Working Practice: you work in a methodical and structured way, with a strong focus on solutions, quality, and ownership; with a proactive approach, you take initiative and tackle complex challenges while working as a team‐oriented who professional thrives in highly collaborative, fast‐paced research and development environments and initiatives contributes to shared success; a strong understanding of AI business models drives you to move beyond prototypes towards fully deployable solutions in close collaboration with business stakeholders Enthusiasm: deep enthusiasm for tackling complex technical challenges and developing novel, impactful solutions within interdisciplinary teams; passion for the synergistic potential between learning and generative Languages: fluent in English, German is a plus

Work-life balance: Flexible working in terms of time, place and working model. Health & Sports: Wide range of health and sports activities. Childcare: Intermediary service for childcare services. Employee discounts: Discounts for employees. Room for creativity: Space for creative work. In-house social counseling and care services: Social counselling and intermediary service for care services.

The recruitment contact or superior will be happy to provide information about the individual benefit plan. .