PhD in Predictive Modelling of Multi-Stressor Interactions for Estuarine Resilience (Environmental Science, Data Science)

Heriot-Watt University is offering a fully funded NERC PhD project focused on predictive modelling of multi-stressor interactions and their influence on estuarine hydro-ecological resilience. The project aims to advance data-driven understanding of how climate and anthropogenic pressures impact estuarine ecosystems, which are vital for ecological services but face complex challenges from pollution, habitat alteration, and over-abstraction. The research bridges hydrology, ecology, and data science, developing physics-informed machine-learning models to predict the effects of multiple environmental stressors on estuarine health. Outcomes will inform adaptive management and policy for sustainable conservation. The successful candidate will gain expertise in data science (R & Python), hydro-ecological and water-quality modelling, climate and environmental data integration, stakeholder engagement, scenario analysis, and policy communication. The project is highly collaborative, based at Heriot-Watt University’s interdisciplinary research community, with opportunities for placements at the UK Centre for Ecology & Hydrology and The Rivers Trust. The supervisory team includes Dr Sandhya Patidar (lead, Heriot-Watt University), Dr Rob Collins (The Rivers Trust), Dr Cédric Laizé, and Dr Michael Hutchins (UK Centre for Ecology & Hydrology). Applicants should have a strong background in environmental science, hydrology, data analytics, or computational modelling, and a passion for climate-driven ecological challenges. The position is fully funded by NERC, covering tuition and stipend, with additional training and collaborative opportunities. Application involves a two-stage process: initial application and motivation, followed by supervisor meetings and project-specific questions for shortlisted candidates. For informal queries, contact [email protected]. Apply via the provided link. Keywords: Estuarine resilience, environmental stressors, data science, hydro-ecological modelling, machine learning, climate change, ecology, environmental science, water quality, predictive modelling.