Current PhD Projects

Our PhD projects integrate cutting-edge AI techniques with bioscience to tackle some of the most pressing challenges in health, nutrition, and sustainability.

Please click on the individual projects listed below to find out more, and to apply for the doctoral projects that are currently available through the NILab programme.

Nutrition and Health

• Advanced AI Methods for Analysing Nutritional Influences on Mental Health
• HAPPI: Harnessing AI for Personalised Nutrition to Promote Healthy Ageing in Older Adults
• Diets to Optimise Individual and Planetary Health (DIPH): A Machine Learning Study

Microbiome and Infection

• Integrating AI and Taxonomy-Free Classification for Enhanced Microbiome Analysis of One Health Environments
• AI-Driven Microbiome Profiling for Predicting Ocular Infection Severity
• EVOLVE-AI: Evolutionary-Informed Deep Learning for Deciphering and Modifying Cell-Cell Communication During Infection, Inflammation, and Cancer
• Integrative multi-omics approaches for exploring host-microbiome interaction and biomarker identification

Antimicrobial Resistance and Drug Discovery

• AI-Driven Discovery and Characterization of Bacterial Secretion Systems and Their Effectors for Addressing Antimicrobial Resistance
• Using AI-Driven Computer Vision and High-Throughput Multiplexed Human Cell Infection Assays to Discover New Antimicrobials
• Development of an AI-Integrated Electrochemical-Plasmonic Biosensor for Detecting AMR Metabolic Signatures
• Decoding Drug Resistance Mechanisms in Acute Myeloid Leukaemia

Emerging AI Applications in Biosciences

• Detection Learning for Discovering and Detecting Virus Signatures
• Generative Deep Learning with Multiomics and 3D Protein Structure for Precision Oncology
• Vector-AI: Knowledge-Driven AI Supports for Vector-Borne Disease Analyses Under Climate Change
• AI for diabetes - enhancing diagnosis and personalised care
• An artificial intelligence approach to investigate neurodegenerative diseases in ageing; interdisciplinary project integrating nutrition, genetics, environmental science and data analytics
• Developing AI platforms for rapid diagnosis of adult and childhood blood cancer