Multimodal Generative AI for Brain Disease Diagnosis

About the Project

International students are also encouraged to apply!

Funding will normally cover UK-level tuition fees, a tax-free stipend for 3-3.5 years at UKRI level (£22,442 for 26/27, £24,238 for 27/28 and £26,177 for 28/29).

International fees waiver will be considered for oversea candidates with demonstrated academic and research background.

Project Description

Early diagnosis of brain diseases such as Alzheimer's, Parkinson's, and multiple sclerosis remains a major clinical challenge: Clinicians must integrate information from multiple sources —MRI, PET, omics and clinical assessments — for a precise early diagnosis, yet sufficient data are often lacking, particularly in the early stages of disease.

This project aims to develop innovative multimodal generative AI methods to enable earlier, more accurate detection of brain diseases. By learning rich representations of brain anatomy, functions, molecular expressions, etc. across different modalities, generative models can sensitively detect early deviations from normal patterns. These approaches can also synthesise missing imaging data, enabling robust diagnosis even when certain scans are unavailable.

The project will bring together expertise in deep learning, medical image analysis, and brain sciences to develop generative AI frameworks that integrate multiple brain scanning modalities and create anomaly detection methods for identifying early disease signatures. To achieve this, our group provides a multidisciplinary research environment spanning AI, machine learning, brain engineering & neuroscience, with access to high-spec GPUs (NV5090, RTX Pro 6000, A100, H100, B200), large-scale brain datasets, and close collaboration with national and international partners. You will have opportunities to explore the following skills and technologies:

• Deep learning and generative AI
• Medical image analysis
• Neuropathological analysis (spatial transcriptomics & synaptome)
• Multimodal data integration and fusion techniques
• Explainable AI and clinical interpretability methods
• Advanced GPU programming
• Working with large-scale brain datasets (UK Biobank, ADNI, Allen Brain Atlas)
• Collaboration with international partners (Edinburgh, McGill, Allen Institute, Microsoft Research )

Training will also be provided in transferable skills including project management, experimental design, data analysis, scientific writing, and presentation skills.

This research has the potential to transform how neurological diseases are detected and monitored and improve patient outcomes through earlier intervention. The developed methods will also advance fundamental understanding of multimodal representation learning in healthcare AI.

More information about the research group can be found at the following links:

https://www.strath.ac.uk/staff/qiuzhenmr/

https://pureportal.strath.ac.uk/en/persons/will-shu

https://www.strath.ac.uk/engineering/biomedicalengineering/

***Application Procedure***

Further information about this project should be sought by emailing the lead supervisor Dr Zhen (Ricky) Qiu (z.qiu@strath.ac.uk), together with one-page Cover Letter, your CV and Academic Transcript. Interview will be held on a rolling basis until the position is filled

Funding Notes

Funding will normally cover UK-level tuition fees, a tax-free stipend for 3.5 years at UKRI norm level (£22,442 for 26/27, £24,238 for 27/28 and £26,177 for 28/29).

The minimum requirement is an Honours degree 2.1 or above, or MSc/MRes in a relevant discipline. Candidates with academic publications in AI or computing-related area will be highly advantageous.

International/oversea students might be possible, but they need to pay the gap between home and international fees.