PhD in AI Pathology for Invasive Lobular Breast Cancer

KU LEUVEN; Belgique; Sur place; EUR 27 000 - 33 000; Plein temps

Résumé du poste

KU Leuven seeks a highly motivated PhD researcher to fine-tune and benchmark AI models for invasive lobular carcinoma using multi-site whole-slide images. You will develop robust representations adaptable across hospitals and scanners, contributing to clinically relevant AI tools.

The role requires a strong computational background, Python proficiency and ML/DL experience, with opportunities to publish in peer-reviewed journals and to collaborate with pathology, radiology and computational teams

Qualifications

• Master's degree in a closely related field (CS/AI/bioinformatics/biomedical engineering/computational biology).
• Strong programming skills, preferably Python, and experience with ML/DL.
• Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning or foundation models.
• Excellent written and spoken English; ability to work in an interdisciplinary, international team.

Responsabilités

• Fine-tune and benchmark pathology foundation models using multi-site data.
• Evaluate representations via retrieval, clustering, classification and external validation.
• Collaborate with computational scientists, pathologists, radiologists and biomedical AI experts.
• Publish results and contribute reusable software, model documentation and papers.

Connaissances

Computational background

Python programming

Machine learning

Deep learning

Computer vision

Digital pathology

Formation

Master's degree

Outils

Python

PyTorch

TensorFlow

OpenSlide

Description du poste

KU Leuven seeks a highly motivated PhD researcher to fine-tune and benchmark AI models for invasive lobular carcinoma using multi-site whole-slide images. You will develop robust representations adaptable across hospitals and scanners, contributing to clinically relevant AI tools.

The role requires a strong computational background, Python proficiency and ML/DL experience, with opportunities to publish in peer-reviewed journals and to collaborate with pathology, radiology and computational teams