PhD Student- Foundation Models for Oncology

Do you have a strong background in machine learning, deep learning, and/or computer vision? Are you curious, independent, and motivated to push the boundaries of AI in a real-world, high-impact domain? Do you enjoy working in an interdisciplinary and international research environment, closely collaborating with other PhD students, clinicians, engineers, and industry partners?

We are looking for an ambitious PhD candidate to work on foundation models for oncology, embedded in the Foundation Models For Oncology (fomo.fo) lab, a collaboration between the Netherlands Cancer Institute (NKI), the University of Amsterdam (UvA), and Kaiko.ai. The position is part of the national NWO Perspectief FIND (Foundation Models for Industry) project.

Foundation models are transforming artificial intelligence by enabling transfer across tasks, data regimes, and application domains. In oncology in particular their potential impact is profound: from earlier and more accurate diagnosis to better treatment planning and longitudinal patient monitoring. However, current foundation models are largely optimized for language or natural images and struggle with the core challenges of medical data, including three-dimensional structure, multimodality, temporal progression, data scarcity, and strict privacy constraints.

This PhD project aims to contribute to a new generation of oncology-native foundation models that can robustly reason over complex multimodal medical data such as radiology, histopathology, and associated clinical information. The research will focus on developing scalable, transferable, and data-efficient learning strategies that move beyond narrow task-specific models and towards general-purpose representations of patient state.

The project is embedded in the national FIND consortium, which brings together multiple Dutch universities, research institutes, and industry partners to advance foundation models for underserved, privacy-sensitive, and application-critical domains. Within FIND, this PhD will specifically focus on the healthcare and oncology track.

What are you going to do?

As a PhD candidate in computer science, you will conduct fundamental and applied research on foundation models for oncology. Together with your supervisors, you will help shape the precise research direction. Your work may include, but is not limited to, the following activities:

• Developing novel foundation model pre-training and post-training methods for oncological data, 3D medical imaging, multimodal patient data, and longitudinal cohorts;

• Designing novel architectures and learning strategies that explicitly model spatial, temporal, and multimodal structure;

• Evaluating models on clinically relevant downstream tasks such as detection, segmentation, structured reporting, and disease progression modelling;

• Performing high-quality academic research at the intersection of deep machine learning, medical imaging, and oncology, with the goal of publishing at top-tier venues such as NeurIPS, ICLR, CVPR, and leading journals;

• Developing robust, well-engineered research code as part of a fast-paced and highly skilled team;

• Collaborating closely with clinicians and researchers at the Netherlands Cancer Institute, as well as academic and industrial partners within UvA, Kaiko.ai, and the broader FIND consortium, to ensure clinical relevance and translational impact;

• Regularly reporting on research progress and presenting intermediate and final results internally and at international conferences and workshops;

• Assisting in relevant teaching and supervision activities;

• Completing and defending a PhD thesis within the official appointment period of four years.

Why the Netherlands Cancer Institute?

At the Netherlands Cancer Institute, we have a shared goal: providing the best care for every patient and every type of cancer. Here we save lives, gain time and quality.

• A Master's degree in Artificial Intelligence, Computer Science, or a related field;

• A strong background in machine learning and computer vision;

• Strong analytical skills and technical skills;

• Excellent programming skills, preferably in Python; experience with full-stack dev ops is a bonus;

• Excellent mathematics foundations, especially statistics and probability theory, calculus and linear algebra;

• You are highly motivated and creative, with an independent mindset and willing to interact with researchers and practitioners from different disciplines;

• Strong communication, presentation, and writing skills and excellent command of English;

• Prior publications in relevant vision and machine learning venues will be a big plus for your application.

Your development opportunities and employment conditions

You work towards a PhD degree from the University of Amsterdam and you will be employed at the Netherlands Cancer Institute. The basis for your employment conditions is in accordance with the CLA Hospitals. You will receive from us:

• A temporary contract for a period of four years, for 36 hours per week;

• A gross monthly salary between €3.738,- and €4.539,-, based on a 36 hours working week, in line with the OIO scale and depending on your experience;

• 144 holiday hours and 57 hours of Personal Life Budget, with full-time employment;

• Annual holiday pay of 8.33% and a fixed end-of-year bonus of 8.33%;

• A travel allowance of €0.23 per km;

• Free parking at the Netherlands Cancer Institute. And with our bicycle plan and discount on public transport we also make it attractive for you to leave the car at home;

• A great opportunity in a specialised hospital where you can also continue to learn and grow yourself if you wish: the AVL Academy offers innovative and inspiring education in the field of oncology professional knowledge, skills and personal development;

• Opportunity to exercise during working hours at our Fit Boutiqs;

• An active PhD association.

Interested?

Please send your application via the application /solliciteer button; applications sent directly by e-mail will not be processed. Incomplete applications will be desk rejected without feedback.

Furthermore, we would like to receive 1 single PDF file with all the documents listed below:

• A motivation letter that explains why you are interested in the PhD vacancy and joining the team;

• A separate research statement where you explain your initial ideas, e.g., how you would propose to start the research. We are not looking at this stage for working solutions; creativity is appreciated.

• Curriculum vitae, including your list of publications if applicable;

• A link to your Master's thesis;

• A link to your GitHub account;

• A complete record of Bachelor and Master courses (including grades and explanation of the grading system);

• A list of projects and publications you have worked on (with brief descriptions of your contributions, max 2 pages);

• The names and contact addresses of at least two academic references (please do not include any recommendation letters).

Would you like to know more about this vacancy or our organisation? Direct any questions to Kevin Groot Lipman, lab manager of the fomo.fo lab at k.groot.lipman@nki.nl. Do you have any questions regarding the application procedure? Please contact Daphne Heemskerk, Recruiter, (d.heemskerk@nki.nl).

This vacancy is open for applications until 22 February 2026.

About us

You will conduct AI research in as part of the fomo.fo lab at both the Netherlands Cancer Institute and the University of Amsterdam (UvA). The principal investigators in the lab are Prof. Dr Lodewyk Wessels (director digital oncology program), dr. Jonas Teuwen (group leader AI for oncology) and dr. Kevin Groot Lipman (clinical implementation lead AI). There will be a close collaboration with Kaiko.ai. The position is with Prof. Dr. Cees Snoek, Professor, head of the Video & Image Sense lab (VIS lab), at the Faculty of Science of the University of Amsterdam. VIS lab is a world-leading lab on Computer Vision and Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty members working on a broad variety of deep learning, computer vision, and foundation model subjects. The position is also embedded in the ELLIS Network of Excellence in AI.

"} #J-18808-Ljbffr