Publisher
source

KU Leuven

PhD in pathology foundation models for invasive lobular breast cancer KU Leuven in Belgium

Degree Level

PhD

Field of study

Computer Science

Funding

Full-time PhD position for three years with an initial probationary period of one year. Appointment and remuneration follow KU Leuven regulations; doctoral training and access to computational infrastructure and datasets are provided.

Deadline

Oct 2, 2026

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Country

Belgium

University

KU Leuven

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Keywords

Computer Science
Biomedical Engineering
Deep Learning
Biology
Artificial Intelligence
Computer Vision
Medical Science
Self-supervised Learning
Digital Pathology
Statistics
ML

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About this position

KU Leuven is advertising a PhD position in pathology foundation models for invasive lobular breast cancer within the Laboratory for Translational Cell and Tissue Research, embedded in the Department of Imaging and Pathology and closely affiliated with University Hospitals Leuven.

The project is part of M4GIC-ILC, a 36-month EIC Pathfinder project coordinated by KU Leuven. It focuses on invasive lobular carcinoma (ILC), a major breast cancer subtype that is under-represented in public datasets and poses challenges for diagnosis, staging, prognosis, relapse prediction, and treatment-response modelling.

The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The work includes self-supervised learning, parameter-efficient fine-tuning, comparison of complementary foundation models, and evaluation through retrieval, clustering, classification, and external validation. The resulting models will support clinically relevant tasks such as virtual generation of diagnostic immunohistochemical stains from H&E, ILC diagnosis and subtype classification, staging, prognosis, relapse prediction, and treatment-response modelling.

This opportunity is especially relevant for candidates with backgrounds in computer science, artificial intelligence, bioinformatics, biomedical engineering, computational biology, statistics, or related fields. Strong Python programming and machine learning/deep learning experience are expected; experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning, or high-performance computing is an advantage.

The position is full-time for three years, with an initial probationary period of one year. The preferred start date is 1 October 2026. The researcher will receive doctoral training at KU Leuven, access to unique multi-site breast cancer datasets and computational infrastructure, and supervision by Prof. Asier Antoranz Martinez and Prof. Giuseppe Floris.

Apply online via the KU Leuven application tool by 2 October 2026 at 23:59 CET. For more information, contact [email protected] or [email protected].

Funding details

Full-time PhD position for three years with an initial probationary period of one year. Appointment and remuneration follow KU Leuven regulations; doctoral training and access to computational infrastructure and datasets are provided.

What's required

Applicants should hold a master's degree in computer science, artificial intelligence, bioinformatics, biomedical engineering, computational biology, statistics, or a closely related field. Strong programming skills, preferably in Python, and experience with machine learning or deep learning are required. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning, or high-performance computing is an advantage. Applicants should be able to work independently and in an interdisciplinary international team, communicate clearly in English, and meet the KU Leuven doctoral programme admission requirements by the starting date.

How to apply

Apply online through the KU Leuven application tool no later than 2 October 2026 at 23:59 CET. For questions, contact the listed supervisors by email.

More information can be found here

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