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PhD in pathology foundation models for invasive lobular breast cancer

KU Leuven · Louvain

Junior 🇬🇧 English
Python Machine learning Deep learning Computer vision Digital pathology Whole-slide image analysis Self-supervised learning Foundation models Multiple-instance learning High-performance computing

Job description

The Laboratory for Translational Cell and Tissue Research at KU Leuven seeks a motivated researcher to develop and fine‑tune pathology foundation models for invasive lobular carcinoma (ILC). The PhD candidate will work with multi‑site H&E and immunohistochemistry whole‑slide images, applying self‑supervised and parameter‑efficient fine‑tuning techniques to capture ILC‑specific morphology while ensuring robustness across hospitals, scanners, and staining protocols. The role involves benchmarking different foundation models, evaluating representations through retrieval, clustering, classification, and external validation, and applying the resulting models to downstream tasks such as virtual stain generation, ILC diagnosis, subtype classification, staging, prognosis, relapse prediction, and treatment‑response modelling. Collaboration with computational scientists, breast pathologists, radiologists, and molecular researchers across the international M4GIC‑ILC consortium is essential, as is the delivery of reusable software, thorough model documentation, and scientific publications. The position offers full‑time doctoral training for three years, access to extensive multi‑site breast cancer datasets, high‑performance computing resources, and opportunities to present at international conferences and publish in peer‑reviewed journals.

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Published 2 weken geleden

Expires over 1 maand

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KU Leuven

Louvain