PhD in Artificial Intelligence And Machine Learning

Start date: 1 October 2026/nDuration: 3.5 years/nFunding source: EPSRC/nFully-funded studentship opportunities covering home and international university fees, additional research training, travel funds and UKRI standard rate (£21,805 for 2026/27 academic year)./nThis PhD project tackles inadequate alignment between heterogeneous medical modalities (radiology images, multiomics/genomics data, physiological time-series) and the language modality used by large multimodal language models. The student will investigate and develop new representation learning and alignment strategies that improve cross-modal grounding and attribution. Potential directions include self-supervised and contrastive pretraining, region- or entity-level alignment objectives, uncertainty-aware grounding, and data-efficient fusion techniques that connect medical evidence to clinically meaningful language outputs. The work will also explore evaluation protocols for clinical faithfulness, including attention/attribution benchmarking and stress tests for hallucinations under distribution shift./nSupported by an interdisciplinary supervisory team spanning multimodal foundation models, self-supervised learning, statistical genetics, and medical AI (Dr. Ahmed, Dr. Demirkan, Dr. Awais).