"Postdoctoral Research Associate in Machine Learning applied to Neuroimaging"

The appointee will join the School of Biomedical Engineering & Imaging Sciences.

About The Role

The research associate will lead the development of cutting-edge multi-modal MRI foundation models. These models will leverage both imaging data and corresponding radiology reports during training to build comprehensive representations that capture the rich, complementary information contained in medical images and clinical text.

The primary focus of this role is to develop foundation models that can be applied downstream to clinical triaging tasks—helping prioritise cases based on MRI imaging data and associated textual information. By integrating visual and language modalities, these models aim to improve the speed, accuracy, and efficiency of interpreting complex MRI scans, ultimately supporting better patient outcomes.

The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information.

By developing these foundation models, you will enable downstream clinical applications focused on triaging adult brain MRI scans—helping healthcare professionals prioritise and interpret MRI scans more efficiently, ultimately improving diagnostic workflows and patient care.

This position offers a unique opportunity to drive innovation at the intersection of AI and medical imaging, making a tangible impact on clinical decision-making and healthcare delivery.

This is a full-time post (35 hours per week), and you will be offered a fixed term contract ideally starting from 2nd January 2026 until 1st Jan 2029.