Potential PhD, MPhil and Master by Research project areas include:

• Large language models, generative AI and foundation models for healthcare, scientific discovery and engineering applications
• Retrieval-augmented generation, GraphRAG, knowledge-grounded reasoning and domain-specific AI systems
• Agentic AI, multi-agent LLM systems, tool-using agents and autonomous decision-support workflows
• Multimodal foundation models integrating text, images, biomedical signals, clinical notes, sensor data and structured health records
• Vision-language models and medical image analysis for diagnosis, monitoring and clinical decision support
• AI and machine learning for clinical risk prediction, personalised healthcare and intelligent decision support
• Biomedical signal processing, physiological modelling, time-series foundation models and representation learning
• Self-supervised learning, transfer learning and domain adaptation for limited-label biomedical and healthcare data
• Robust, explainable and trustworthy AI, including uncertainty quantification, calibration, out-of-distribution generalisation and model reliability
• Evaluation and alignment of LLMs and generative AI systems, including hallucination detection, factuality, safety and clinical reliability
• Causal machine learning, interpretable modelling and counterfactual reasoning for healthcare and engineering applications
• Privacy-preserving AI, federated learning and secure machine learning for sensitive clinical and biomedical data
• Efficient AI, model compression and scalable deployment of foundation models in real-world healthcare and engineering environments
• MLOps, model monitoring, drift detection and responsible deployment of AI systems in production settings
• AI-driven innovation across medicine, biomedical engineering, data science and intelligent systems