Join us!
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. The question is not only “what happens next?” 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.
This is what you will do
You will help define and build the next generation of world models and the agents that act in them. Concretely, you will:
- develop mechanism-informed world models that extract events, causes and temporal/causal relationships from observation, rather than only fitting pixels or trajectories;
- design ways to tokenize and learn latent actions and dynamics, and to embed controllable inductive biases into world models and agents;
- build agents that plan, reason and act inside these models — and that remain controllable, auditable and interpretable;
- validate your methods across domains: robot learning and manipulation, embodied agents, and biomedical dynamics (e.g. mechanism-informed generative models of biological processes), demonstrating that the same principles transfer;
- publish at top-tier venues (CVPR, ICLR, ICML, NeurIPS, ECCV) and present your work at international meetings;
- contribute to the life of the lab — reading groups, seminars, collaborations — and assist in teaching at the bachelor and master level.
What we ask of you
You are curious and rigorous; when a result is surprising, you dig until you understand the mechanism rather than settling for the metric. When experiments get messy you keep your cool, and you enjoy working in an interdisciplinary, international team.
Your experience and profile:
- an MSc (completed before the start date) in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, (Applied) Mathematics, or a closely related field;
- a strong foundation in machine learning / deep learning and solid programming skills (e.g. Python, PyTorch);
- an affinity for one or more of: world models, generative modelling, causal/representation learning, reinforcement or imitation learning, dynamical systems, or robotics;
- the ability to formulate and pursue research questions independently, and to communicate them clearly;
- a professional command of English (the working language of the lab); willingness to learn some Dutch is welcome but not required.
It is a plus if you additionally have experience with robotics, physics engines or simulators, causal inference, or biomedical / scientific data — and any track record of research output (thesis, preprints, open-source code) is appreciated.
This is what we offer you
A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for 18 months and, after a satisfactory evaluation, extended for a total of 4 years). The preferred starting date is as soon as possible / to be discussed. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduate and master students.
The gross monthly salary, based on 38 hours per week, ranges between € 3,059 in the first and € 3,881 in the final year (scale P), in line with the Collective Labour Agreement of Universities of the Netherlands. This does not include 8% holiday allowance and 8.3% year-end allowance. The UFO profile of PhD candidate (Promovendus) is applicable.
Curious about our extensive secondary benefits package? You can read more about it on the UvA website.
You will work in this team
The Faculty of Science has a student body of around 8,000, as well as 1,800 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 organisation? Read more about working atthe University of Amsterdam.
Want to teach machines the mechanisms behind how the world changes — and build agents that act on them?
Join us!
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. The question is not only “what happens next?” 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.
This is what you will do
You will help define and build the next generation of world models and the agents that act in them. Concretely, you will:
- develop mechanism-informed world models that extract events, causes and temporal/causal relationships from observation, rather than only fitting pixels or trajectories;
- design ways to tokenize and learn latent actions and dynamics, and to embed controllable inductive biases into world models and agents;
- build agents that plan, reason and act inside these models — and that remain controllable, auditable and interpretable;
- validate your methods across domains: robot learning and manipulation, embodied agents, and biomedical dynamics (e.g. mechanism-informed generative models of biological processes), demonstrating that the same principles transfer;
- publish at top-tier venues (CVPR, ICLR, ICML, NeurIPS, ECCV) and present your work at international meetings;
- contribute to the life of the lab — reading groups, seminars, collaborations — and assist in teaching at the bachelor and master level.
What we ask of you
You are curious and rigorous; when a result is surprising, you dig until you understand the mechanism rather than settling for the metric. When experiments get messy you keep your cool, and you enjoy working in an interdisciplinary, international team.
Your experience and profile:
- an MSc (completed before the start date) in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, (Applied) Mathematics, or a closely related field;
- a strong foundation in machine learning / deep learning and solid programming skills (e.g. Python, PyTorch);
- an affinity for one or more of: world models, generative modelling, causal/representation learning, reinforcement or imitation learning, dynamical systems, or robotics;
- the ability to formulate and pursue research questions independently, and to communicate them clearly;
- a professional command of English (the working language of the lab); willingness to learn some Dutch is welcome but not required.
It is a plus if you additionally have experience with robotics, physics engines or simulators, causal inference, or biomedical / scientific data — and any track record of research output (thesis, preprints, open-source code) is appreciated.
This is what we offer you
A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for 18 months and, after a satisfactory evaluation, extended for a total of 4 years). The preferred starting date is as soon as possible / to be discussed. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduate and master students.
The gross monthly salary, based on 38 hours per week, ranges between € 3,059 in the first and € 3,881 in the final year (scale P), in line with the Collective Labour Agreement of Universities of the Netherlands. This does not include 8% holiday allowance and 8.3% year-end allowance. The UFO profile of PhD candidate (Promovendus) is applicable.
Curious about our extensive secondary benefits package? You can read more about it on the UvA website.
You will work in this team
The Faculty of Science has a student body of around 8,000, as well as 1,800 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 organisation? Read more about working atthe 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 — e.gavves@uva.nl | www.egavves.com |
