At Bosch Research, we are driving the next generation of physical AI – intelligent systems that can perceive the real world, reason, and act robustly. From automated driving to robotics, our goal is to develop AI technologies that are not only powerful but also safe, efficient, and scalable. Despite remarkable progress in foundation models and end-to-end learning, today's autonomous systems still struggle to reliably generalize to new situations and often require enormous amounts of data and significant computing resources. For Bosch products, however, AI solutions must also deliver strong performance under real-world conditions – such as limited computing power, high energy efficiency, safety, and reliability.

In this doctoral project, you will explore novel approaches to developing the next generation of efficient autonomous decision-making systems. Your research combines ideas from imitation learning, reinforcement learning, large-scale simulation, world models, and model compression to develop AI agents (across multiple types) that continuously improve, adapt to novel situations, and effectively transfer from simulation to reality. A key focus of the project is enabling resource-efficient physical AI: achieving the reliability and generalizability of today's leading AI systems while significantly reducing the computational demands required for deployment on real-world devices.

By joining Bosch Research, you'll have the opportunity to work at the interface of cutting-edge AI research and industrial practice. You'll collaborate with leading experts from academia and industry to help shape the future of intelligent products and services.

• Education: Excellent Master's degree in Computer Science, Robotics, Mathematics, Physics or a comparable field of study
• Experience and knowledge: Extensive practical experience and sound theoretical understanding in the field of Reinforcement Learning (RL), ideally with expertise in emergent behavior, Safe RL, Offline RL and/or Multi-Agent RL, combined with excellent Python skills and in-depth knowledge of industry-standard ML frameworks (e.g., PyTorch, Hugging Face, JAX, TensorFlow)
• Experience and skills: Experience in software development in larger teams and in providing ML-based systems for real-world applications; knowledge of MLOps and CI/CD is an advantage.
• Experience and skills: Good knowledge of C and related frameworks (e.g., ROS 2)
• Experience and knowledge: Publications at leading conferences in machine learning, robotics or computer vision (e.g. NeurIPS, ICML, ICLR, CVPR, IROS, ICRA) are advantageous.
• Personality and working style: You work methodically and in a structured manner, with a strong focus on solutions, quality, and taking responsibility; with a proactive approach, you take initiative and master complex challenges as a team-oriented professional who feels comfortable in highly collaborative, dynamic research and development environments and actively contributes to shared success; a strong understanding of AI business models motivates you to go beyond prototypes and develop fully deployable solutions in close collaboration with business stakeholders.
• Enthusiasm: Strong enthusiasm for solving complex technical challenges and developing innovative, impactful solutions in interdisciplinary teams; passion for the synergistic potential between reinforcement learning and generative AI
• Languages: Fluent English, German is an advantage

As long as this position is advertised in our job portal, we are looking for suitable candidates.

For data protection reasons, applications are only accepted via our application portal. We regret that we cannot consider applications submitted by email or post.