PhD Studentship: Advanced Uncertainty Quantification for decision support: Balancing risks and preferences for policy decisions. 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

Policymakers and landowners must make complex, high-stakes decisions under considerable uncertainty, with long-term societal and environmental impacts. For instance, achieving the UK’s Net Zero Carbon goal by 2050 requires land-use strategies —such as afforestation. These strategies are developed amid uncertainties in climate change, economic dynamics, and regulatory shifts. This project focuses on advancing decision science by developing tools that rigorously quantify uncertainties across inputs, models, and statistical frameworks, combining environmental processes with economic behaviours. Leveraging recent advances in uncertainty quantification and linking to preference elicitation, we will codevelop transparent, responsive tools with end users that integrate their preferences into the way alternative strategies are evaluated, grouped and navigated through, empowering decision-makers to explore diverse strategies and associated risks through a lens tailored to their preferences.

This project addresses key challenges in creating adaptive, uncertainty-informed decision-support for policymakers and land managers. There is demand for easy-to-use decision support integrating leading scientific knowledge of environmental processes, and economic behaviour with explicit quantification of different sources of uncertainty (e.g. input, modelling and statistical).

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