PhD candidate AI-based pan-cancer prediction of 'omics biomarkers from histopathology

• Application Deadline: May 7 2026
• Nijmegen
• On-site

Job Description

Job description

Molecular alterations such as Microsatellite Instability (MSI) Homologous Recombination Deficiency (HRD) and specific gene mutations play a critical role in modern oncology. They are essential omics biomarkers for selecting patients for targeted therapies and immunotherapies. Currently determining the presence of these biomarkers requires expensive tissue-consuming and time-intensive molecular testing such as next-generation sequencing. However these underlying genomic alterations often manifest as distinct morphological patterns within the tumor and its microenvironment.

In this project you will harness the power of artificial intelligence to predict these crucial omics biomarkers directly from routinely available hematoxylin and eosin (H&E)-stained histopathology slides. Because biomarkers like MSI have therapeutic relevance across many solid tumor types your research will take a pan-cancer approach. You will leverage state-of-the-art pathology foundation models and weakly-supervised learning techniques to extract robust representations from gigapixel whole-slide images learning directly from clinical ground-truth labels without the need for exhaustive manual annotations.

Furthermore clinical adoption of AI requires transparency. A major focus of your project will be developing and evaluating explainability methods. By opening the black box you will help pathologists and oncologists understand the morphological features driving the AIs predictions building clinical trust and potentially discovering novel visual correlates of underlying genomic alterations.

Tasks and responsibilities

• Develop and optimize weakly-supervised deep learning algorithms and adapt pathology foundation models to predict omics biomarkers (e.g. MSI) across diverse cancer types.
• Investigate and pioneer explainability techniques to make complex neural network predictions transparent and interpretable for clinical end-users.
• Process and analyze massive multi-centric datasets of oncological whole-slide images with matched molecular ground-truth data.
• Collaborate closely with pathologists oncologists and (inter)national machine learning researchers to validate the clinical and biological relevance of your developed algorithms.
• Have fun interactions with colleagues present at local and (inter)national conferences publish in high-impact technical and medical journals and develop yourself as an independent researcher.

Place of work

Computational Pathology Group

The Computational Pathology Group is a research group of the department of Pathology of the Radboud University Medical Center (Radboudumc). We are also part of the cross-departmental Diagnostic Image Analysis Group (DIAG) at Radboudumc with researchers in the departments of Radiology and Nuclear Medicine Pathology and Cardiology.

We develop validate and deploy novel medical image analysis methods usually based on deep learning technology and focusing on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast colon prostate and lung cancer among others. Our group is among the international front runners in the field evidenced for instance by the highly successful CAMELYON and PANDA Grand Challenges which we organized and published in JAMA and Nature Medicine.

At the department of Pathology you examine cells and tissues to accurately diagnose diseases. You support physicians within Radboudumc and beyond by providing fast and reliable diagnostic results. Using modern techniques digital microscopy and AI applications you work toward increasingly precise addition you contribute to internationally recognized research education and the training of new specialists.