About this role
Go to the homepage of Grow at the University of Amsterdam /static/uploads/53 universiteit van amsterdam re c thayana taveres 24092020 2811.jpg Want to teach machines the mechanisms behind how the world changes - and build agents that act on them? World models are controllable, physics- and mechanism-grounded simulators of reality: they let an agent rehearse the consequences of its actions thousands of times before it acts in the real world. Paired with agents that plan, reason and act inside them, they are emerging as the substrate of Physical AI - and they cut across domains that look different on the surface but share the same scientific core: a robot rehearsing a manipulation task in a digital twin, and a biomedical model simulating how a tumour responds to therapy across biological scales, are both betting that structure beats brute scale. In this PhD you will develop mechanism-informed world models: models that do not merely predict the next frame, but discover the underlying events, causes and temporal relationships that drive change - and expose them as controllable, intervenable structure. but "what are the latent actions and mechanisms, and how do we tokenize, learn and steer them in a controllable manner?" This is one of the least-explored and most consequential frontiers in Physical AI. World models are controllable, physics- and mechanism-grounded simulators of reality: they let an agent rehearse the consequences of its actions thousands of times before it acts in the real world. Paired with agents that plan, reason and act inside them, they are emerging as the substrate of Physical AI - and they cut across domains that look different on the surface but share the same scientific core: a robot rehearsing a manipulation task in a digital twin, and a biomedical model simulating how a tumour responds to therapy across biological scales, are both betting that structure beats brute scale. In this PhD you will develop me...