Learning systems are systems that can adjust and adapt their behavior to influences from their environment.

Several systemic research fields, which pose central questions on the understanding of complex systems, from recognition, to learning, to adaptation, are investigated within the Max Planck ETH Center for Learning Systems:

  • Processes of (self-)organization, (machine) learning and artificial intelligence of complex systems.
  • Technologies to probe intelligent biological systems and their ability to adapt to varying external dynamics, including the nervous system and new computational, mathematical and robotic models of such systems.
  • Advanced technologies – from micro to macro – to design, fabricate, assemble, control, and interrogate complex synthetic, bio-inspired or bio-hybrid systems.
  • Robust model-based control of intelligent behavior.
  • Stimulus-response, as well as perception-action-cycles of autonomous systems.
  • Learning to perceive in and adapt to complex time-varying environments.

ETH Zurich and the Max Planck Institute for Intelligent Systems have strong groups with an international visibility in all of these areas. The joint research center allows these groups to join forces with the common goal to investigate, understand and master the challenges of intelligence, learning and control in the zettabyte age. Since 2025, they are joined in CLS by the ELLIS Institute Tübingen as an Associated Partner. Further project supervision is offered by CLS Senior Fellows based at the Max Planck Institute for Informatics.

CLS hosts research activities that build our understanding of complex systems via the pursuit of nine central goals.

G1 Machine Learning and Empirical Inference of Complex Systems
G2 Perception-Action-Cycle for Autonomous Systems
G3 Robust Model-Based Control for Intelligent Behavior
G4 Robust Perception in Complex Environments
G5 Design, Fabrication, and Control of Synthetic, Bio-Inspired, and Bio-Hybrid Robotic Systems
G6 Neurotechnology and Emergent Intelligence in Nervous Systems
G7. Natural language Processing, Large Language Models (LLMs), and Reasoning
G8. Multi-modal Foundation Models
G9. Machine Learning and Society: Fairness, Transparency, and Accountability.

"Our fundamental research does not only bring benefits in the distant future - we answer pressing questions that arise today" - Thomas Hofmann, Founding Co-​Director.