Quality Control with Foundation Models for Radiology

Internship / Student Project Opportunity

We are developing foundation models for large-scale radiology data with a focus on robust representation learning and quality control.

The project explores how to learn general-purpose imaging representations that transfer across tasks such as scan-level quality assessment. We are particularly interested in self-supervised and weakly supervised learning strategies that can leverage large, heterogeneous radiology datasets.

During the project, students may work on:

• pretraining and adapting radiology foundation models on large-scale imaging data;
• learning transferable visual representations for downstream diagnostic tasks;
• designing quality control pipelines to detect low-quality scans and out-of-distribution inputs;
• evaluating robustness, calibration, and generalization across sites and cohorts.

We welcome applications from candidates with a strong machine learning and coding background (for example, Python/PyTorch, deep learning, and practical experience handling medical imaging data).

This is suitable as an internship or student research project for candidates interested in clinically relevant AI and translational machine learning.

How to apply

Please email the following documents:

• CV
• Academic transcript
• A short motivation letter

Send your application to: yigit.avci@kcl.ac.uk