The University of Manchester is offering a fully funded PhD position in Multimodal Agentic AI for Environmental Exposure and Respiratory Disease, supervised by Associate Professor Mobarak Hoque and Professor David Topping. This cross-faculty project bridges the Faculty of Biology, Medicine and Health (FBMH) and the Faculty of Science and Engineering (FSE), integrating artificial intelligence, environmental science, and respiratory health research.

The research will focus on developing multimodal large language model (LLM) based agentic systems capable of reasoning over heterogeneous data sources, including environmental exposure measurements, atmospheric modelling outputs, health records, and clinical indicators of respiratory disease severity. The successful candidate will design trustworthy AI models that integrate structured and unstructured data, perform cross-modal reasoning, and provide interpretable insights into how environmental factors influence disease progression. The project aims to advance the understanding of the impact of air pollution, weather, and indoor exposure on respiratory health, with potential applications in studying COVID-19 severity and broader environmental health impacts.

The supervisory team includes Dr. Mobarak Hoque, an expert in multimodal agentic AI for healthcare, and Prof. David Topping, with expertise in environmental modelling. The project is ideal for candidates with a strong background in computer science, AI, data science, or related fields, and an interest in health informatics or environmental science. Experience with large language models, machine learning, or multimodal data integration is highly desirable. Applicants should hold a first-class or upper second-class undergraduate degree (or equivalent) and meet the University of Manchester’s English language requirements.

This PhD is funded through the President’s Doctoral Scholarship, covering full tuition fees and providing a stipend for living expenses. The application deadline is March 16, 2026. For more information and to apply, visit the project page on FindAPhD or contact the supervisors directly. The research group is internationally recognized for pioneering work in medical imaging AI, safe and trusted AI, and biomedical data science, with a strong record of publications and awards.

Keywords: multimodal AI, large language models, environmental exposure, respiratory disease, agentic systems, health informatics, environmental science, atmospheric modelling, clinical data integration.