PhD Studentship: Physics-informed Machine Learning for decision making related to future extreme weather events. UNRISK NERC Centre for Doctoral Training PhD studentship 2025/26 Entry

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them with a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Climate risk analysis aims to understand and predict extreme weather events, such as severe precipitation, which might become more frequent and intense due to climate change. Probabilistic machine learning (ML) models, especially those capable of producing realistic spatiotemporal output, are useful tools to estimate the risk of short-lived localised weather events based on large scale climate variables. A recent advancement in data-driven ML is the inclusion of physical principles, such as physics-informed neural networks (PINNs), to improve model accuracy and realism by constraining model output to obey known physical laws. Physics-based ML models can potentially capture the complex processes that drive severe weather, but many challenges remain in ensuring their practical applicability. Ongoing research aims to refine physics-based probabilistic ML models to support decision-making processes in risk assessment, disaster preparedness, and insurance.

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