Postdoctoral Researcher in Physics-Informed World Models and Agents

Bring physics into world models and make embodied agents fast reliable and ready for the real world!

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 postdoc you will lead research on physics-informed (and physics-grounded) world models and agents: how to consolidate and transfer physical knowledge into learning systems for the real world. Physics engines and physics-informed neural networks are powerful but the open problems are exactly where they stop being sufficient under partial observation in complex regimes that demand orders-of-magnitude speed-up and where knowledge must transfer across embodiments scenes and scientific domains. This is where world models earn their keep.

You will help define and build the next generation of world models and the agents that act in them. Concretely you will:

• develop physics-informed and physics-grounded world models that remain reliable under partial observation where pure physics engines fall short;
• design methods that achieve large speed-ups in complex settings so that agents can rehearse and plan at scale;
• study how to consolidate and transfer physical knowledge across embodiments scenes and domains from robot manipulation to biomedical and scientific dynamics;
• build and evaluate agents that act inside these models with an emphasis on controllability auditability and safety;
• lead and co-author publications at top-tier venues (CVPR ICLR ICML NeurIPS ECCV) and represent the lab at international meetings;
• help shape the research agenda co-supervise PhD and MSc students and contribute to proposals and collaborations (e.g. with industry and clinical partners).

You are an independent researcher who can carry a line of work from idea to publication and you enjoy mentoring others. You communicate clearly across disciplines and you are energised not deterred by problems that sit between physics learning and the messy real world.

Your experience and profile:

• a PhD (completed or near completion) in Machine Learning Computer Vision Robotics Physics Applied Mathematics or a closely related field;
• a strong publication record at leading venues in machine learning computer vision robotics or scientific ML;
• demonstrated expertise in one or more of: physics-informed learning world models simulation / physics engines dynamical systems robot learning or scientific machine learning;
• excellent programming skills (e.g. Python PyTorch) and experience running substantial experiments;
• the ability to drive research independently and to mentor PhD and MSc students;
• a professional command of English; willingness to learn some Dutch is welcome but not required.

It is a plus if you additionally have hands-on robotics experience experience with differentiable / GPU-parallel simulation (e.g. Isaac Lab MuJoCo MJX Genesis) or experience applying physics-informed methods to biomedical or scientific data.

A temporary employment contract for 38 hours per week for a period of 21 months with a probationary period of two months. The preferred starting date is as soon as possible / to be discussed. If we assess your performance positively an extension is possible.

The gross monthly salary based on 38 hours per week and dependent on relevant experience ranges between 3546 to 5538 (scale 10).This does not include 8% holiday allowance and 8.3% year-end allowance. The UFO profile of Researcher 4 is applicable. The Collective Labour Agreement of Universities of the Netherlands is applicable.

Curious about our extensive secondary benefits package You can read more about it on the UvA website.

The Faculty of Science has a student body of around 8000 as well as 1800 members of staff working in education research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works be it elementary particles the birth of the universe or the functioning of the brain.

You will join the CyPhai Cyberphysical AI Lab led by Prof. Stratis (Efstratios) Gavves part of VISLab (Video & Image Sense Lab) at the Informatics Institute (IvI) Faculty of Science University of Amsterdam. CyPhai pursues a single north star: algorithms that understand the dynamics of the physical world and let embodied agents act safely reliably and accountably.

The lab sits at the intersection of an unusually broad set of programmes that all converge on the same loop reconstruct imagine act reward monitor. These span robot learning (compositional world models and robot imitation learning with an extended collaboration with Toyota Research) the QUVA 2.0 lab with Qualcomm the POP-AART lab with Elekta and the Netherlands Cancer Institute and the AIRIS programme on mechanism-informed generative models for predictive and personalised medicine. CyPhai is part of the ELLIS network of excellence in AI.

You will be embedded in a vibrant international team of PhD students and postdoctoral researchers with access to substantial GPU compute real robots and rich biomedical and physical-world data. You will publish at the leading venues in the field CVPR ICLR ICML NeurIPS ECCV and present your work internationally.

Want to know more about our organization Read more about working at the University of Amsterdam.

If this profile fits you and you are excited by the challenge we look forward to receiving your application. You can apply online via the button below. We accept applications until and including 20 August 2026.

Applications should include the following (all files besides your CV should be submitted in one single PDF file):

• a detailed CV including the months (not just years) when referring to your education and work experience;
• a letter of motivation explaining why this position fits you;
• a list of publications and/or representative project work (e.g. MSc thesis code preprints) if available;
• the names and email addresses of two references who can provide letters of recommendation.

A knowledge security check can be part of the selection procedure
(for details:national knowledge security guidelines)

Only complete applications received within the response period via the link will be considered.

Questions or need more information Please contact:

• Prof. Stratis (Efstratios) Gavves CyPhAI Cyberphysical AI Lab Informatics Institute University of Amsterdam