PhD Position on Dynamical Models Learning for 3D Cell Imaging Data

What you will do

You will work at the intersection of computer vision, machine learning and biomedical imaging to:

• Design methods that groups pixels into cells and links them over hours-long and non-trivial dynamics in 3D.
• Integrate Segment Anything / Segment Anything 2 priors and physics-informed constraints to boost robustness in crowded 3D scenes.
• Learn cell tracks into dynamics-aware representations that discover and detect salient cell behaviours (e.g. serial killing) directly from the learnt spatio-temporal graph.
• Benchmark the new tracker against academic baselines on BEHAV3D datasets and public cell-tracking challenges.
• Release code, pretrained models and a user-friendly demo to the community.

Your work will be embedded in an interdisciplinary team of imaging scientists, immunologists and AI researchers, giving you access to world-class microscopes, GPU clusters and rich annotated datasets.

Tasks and responsibilities:

• Conduct independent research leading to high-impact publications and open-source software.
• Collaborate with experimental partners to acquire, curate and analyse large-scale 3D time-lapse data.
• Present findings at internal meetings, international conferences and consortium workshops.
• Contribute up to 10% of your time to teaching (e.g. supervising MSc/BSc projects, assisting practicals).
• Complete and defend a PhD thesis within four years.

Your profile

• MSc in Computer Science, AI, Applied Mathematics, Biomedical Engineering or a related field.
• A strong background/knowledge in reinforcement learning, causal learning or multimodal modals is a big plus.
• Solid background in deep learning, graph neural networks or visual object tracking; experience with PyTorch or JAX is a plus.
• Experience with AI/HPC supercomputing and running software on scale.
• You are highly motivated, independent, and creative.
• Strong communication and collaboration (including to non-technical part of the team), presentation and writing skills and excellent command of English.
• Prior publications in relevant machine learning conferences or journals are advantageous.

Our offer

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is 1 October 2025. 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 undergraduates and master students.

The gross monthly salary, based on 38 hours per week  ranges between € 2,901 in the first year to € 3,707 in the last (scale P). This does not include 8% holiday allowance and 8,3% year-end allowance. The UFO profile PhD Candidate is applicable.  A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Universities of the Netherlands is applicable.

Besides the salary and a vibrant and challenging environment at Science Park we offer you multiple fringe benefits:

• 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
• multiple courses to follow from our Teaching and Learning Centre;
• a complete educational program for PhD students;
• multiple courses on topics such as leadership for academic staff;
• multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses;
• 7 weeks birth leave (partner leave) with 100% salary;
• partly paid parental leave;
• the possibility to set up a workplace at home;
• a pension at ABP for which UvA pays two third part of the contribution;
• the possibility to follow courses to learn Dutch;
• help with housing for a studio or small apartment when you’re moving from abroad.

Are you curious to read more about our extensive package of secondary employment benefits, take a look here.