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.    

• 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 reinforcement 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 in 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 professional who thrives in highly collaborative, fast‑paced research and development environments and actively 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 reinforcement learning and generative AI
• Languages: fluent in English, German is a plus

https://www.bosch-ai.com
www.bosch.com/research

The final PhD topic is subject to your university.

Start: September 2026

Please submit all relevant documents (incl. curriculum vitae, certificates).

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need support during your application?
Celina Dannecker (Human Resources)
+49 711 811 21346

Need further information about the job?
Luigi Palmieri (Functional Department)
+49 711 811 7584

Work #LikeABosch starts here: Apply now!