Max Planck Institute for Intelligent Systems Campuses

The Stuttgart campus specializes in the physical realization of intelligent systems, bridging theory with hardware through expertise in robotics, materials science, and human-machine interaction. It emphasizes embodied intelligence, developing soft robots, haptic interfaces, and bio-hybrid systems, integrated with IMPRS-IS PhD programs for hands-on training.

• Physical Intelligence: Micro- and nano-robotics, soft actuation, and magnetic manipulation for medical applications.
• Haptic Intelligence: Tactile sensing, human-robot touch interaction, and realistic haptic rendering.
• Robotic Materials: Smart materials, programmable matter, and responsive microstructures.
• Micro, Nano, and Quantum Systems: Femtosecond laser processing, 3D nano-printing, and quantum materials for sensing.

Training includes practical courses on robot design, fabrication techniques, biomechanical modeling, and control systems. Students participate in labs developing next-gen prosthetics and swarm robotics, collaborating with University of Stuttgart on engineering challenges in intelligent automation.

The Tübingen campus of the Max Planck Institute for Intelligent Systems focuses on the theoretical and computational foundations of intelligent systems, emphasizing how these systems perceive, learn, and make decisions. Research here integrates machine learning, computer vision, and neuroscience to model intelligent behavior, with strong ties to the International Max Planck Research School for Intelligent Systems (IMPRS-IS), offering PhD training in advanced topics.

• Machine Learning and Probabilistic Modeling: Courses and research in deep learning, kernel methods, causal inference, and reinforcement learning.
• Perceiving Systems: Computer vision, human body modeling, scene understanding, and motion capture technologies.
• Empirical Inference: Statistical learning theory, Gaussian processes, and large-scale data analysis.
• Computational Neuroscience: Neural information processing, perceptual inference, and brain-inspired AI.
• Social Foundations of Computation: Game theory, multi-agent systems, and decision-making under uncertainty.

PhD students engage in interdisciplinary projects, attending seminars on modern ML techniques, Bayesian methods, and generative models. The campus hosts workshops on topics like neural networks and ethical AI, fostering collaboration with University of Tübingen.