Company Description BMW Group is a global leader in premium automobiles, motorcycles, and financial services, represented by the BMW, MINI, Rolls-Royce, and BMW Motorrad brands. With over 30 production sites and a sales network spanning more than 140 countries, the company offers an international and innovative working environment. In 2025, BMW Group delivered 2.46 million passenger vehicles and more than 202,500 motorcycles worldwide, generating €133.5 billion in revenue and employing over 154,000 people. The company’s long-term success is driven by responsible action and a strong commitment to sustainability across the entire lifecycle of its products, from sourcing and production to end-of-life.
Role Description The PhD Researcher in Physical Artificial Intelligence and Embodied Robotics will conduct original research at the intersection of AI, robotics, and advanced materials, with the goal of enabling intelligent, adaptive physical systems for future mobility applications. Working full time on-site in Munich, the researcher will design, prototype, and experimentally validate embodied robotic concepts, including soft or bio-inspired robots, sensor-rich structures, and physically intelligent components. Daily activities include developing and testing algorithms for perception, control, and learning in physical systems, building and instrumenting robotic platforms, and analyzing experimental data. The role involves close collaboration with interdisciplinary teams across BMW Group, academic partners, and external research institutions, as well as publishing results in high-impact journals and presenting at international conferences. The researcher will contribute to transferring promising concepts into automotive and manufacturing use cases, while documenting work clearly for both scientific and industrial stakeholders.
Qualifications
- Strong foundation in robotics and control, including experience with embodied or soft robotics, dynamic systems, and mechatronic design.
- Expertise in artificial intelligence and machine learning methods relevant to physical systems, such as reinforcement learning, adaptive control, or model-based planning.
- Proficiency in computational tools and programming (e.g., Python, C++), simulation environments (e.g., ROS, Gazebo, MATLAB), and data analysis for experimental robotics.
- Background in physical sciences or engineering related to materials, mechanics, or sensing, such as mechanical engineering, electrical engineering, computer science, physics, or a closely related field.
- Ability to design and execute rigorous experiments, including prototyping, hardware integration, instrumentation, and evaluation of real-world robotic systems.
- Strong scientific communication skills, with experience writing research papers, documenting results, and presenting complex technical concepts to diverse audiences.
- Demonstrated capacity for independent, curiosity-driven research combined with effective collaboration in multidisciplinary and international teams.
- Master’s degree (or equivalent) in a relevant discipline and eligibility or willingness to enroll in a