Research area Statistical Physics & Machine Learning

Harnessing the physics of complex systems

Statistical Physics and Machine Learning

This research focuses on complex systems characterized by emergent collective phenomena such as critical behaviour, learning, and self-organisation.


This research area advances statistical physics and machine learning for novel applications. We design materials that can undergo dramatic, controllable changes to their properties – including magnetic, optical, and rheological. We develop new methodologies to characterize long-range forces and strongly-coupled systems. We also strive to understand the collective behaviour of complex systems across vastly different scales, from molecules to whole populations. As part of this research, we design efficient and reliable quantum and classical algorithms to tackle their complexity.
The statistical physics and machine learning research area investigates the following topics:
– Quantum Information Theory
– Theoretical Chemical Physics
– Theory of Mesoscopic Quantum Systems
– Complex Systems and Statistical Mechanics
– Physics of Active Matter
– Experimental Soft Matter Physics
– Multifunctional Ferroic Materials.

Research groups