PhD Position in Embodied World Models, AI, and Computer Vision at University of Amsterdam University of Amsterdam in Netherlands

The University of Amsterdam is offering a PhD position in Embodied World Models within the Faculty of Science, located in Amsterdam, Netherlands. This research opportunity focuses on artificial intelligence, deep learning, and computer vision, with the goal of equipping robots to perceive and act in open, unpredictable environments. Key research areas include test-time generalization, embodied grounding, data scarcity, and uncertainty modeling. The successful candidate will collaborate with the OpenBots Lab (a partnership between UvA and TU Delft), TNO, and the Royal Netherlands Marechaussee.

Supervision will be provided by Prof. dr. Cees Snoek (VIS Lab, UvA) and Dr. ir. Gertjan Burghouts (TNO). The position is full-time (38 hours per week) for up to 4 years, starting with an initial 18-month contract that can be extended. The salary ranges from €3,059 to €3,881 per month, with additional benefits including an 8% holiday allowance and an 8.3% year-end bonus. The role offers opportunities for participation in courses, workshops, and conferences, as well as teaching experience and career development in an inclusive, international research environment.

Applicants must hold an MSc in Artificial Intelligence, Computer Science, Engineering, Physics, Mathematics, or a related field. A strong background in machine learning and computer vision is essential, and experience in robotics is considered a plus. Candidates should have a solid foundation in statistics, calculus, and linear algebra, excellent programming skills (preferably Python), and be highly motivated, independent, creative, and strong communicators. Prior publications in machine learning or computer vision are advantageous. Due to security clearance requirements, applicants must reside in NATO countries.

To apply, candidates should prepare a single PDF containing a motivation letter (max 2 pages), CV (max 2 pages, including publications), research statement (max 2 pages), master thesis abstract or link, complete academic transcripts, a list of projects/publications (max 1 page), and the names and contact details of two academic references. The application deadline is 13 March 2026. For further information, contact Prof. dr. Cees Snoek ([email protected]) or Dr. ir. Gertjan Burghouts ([email protected]).