PhD in Ecotoxicology

Flood risk and water pollution are escalating challenges under climate change and rapid urbanisation. Conventional grey infrastructure provides limited flexibility and sustainability, while nature-based solutions (NbS)—such as wetlands, riparian buffers, and urban green corridors—offer multiple co-benefits by reducing flood hazards, improving water quality, and supporting biodiversity. Yet, designing NbS remains challenging due to diverse local conditions, competing objectives, and deep uncertainties. This project will develop an AI-assisted framework for NbS design and evaluation, combining advanced predictive modelling with multi-objective optimisation. Methodological approaches include data integration of remote sensing, hydrological, water quality, and land-use datasets, AI-based modelling, multi-objective optimisation, and uncertainty quantification through Bayesian inference and factorial analysis. The project will deliver a transferable decision-support tool for policymakers and planners, advancing AI applications in sustainability science and contributing to resilient, cost-effective solutions for water security.