PhD Studentship: Modelling biodiversity responses to climate and human-induced land change. 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

Biodiversity loss is threatening ecosystems and the services they provide to human society. To reverse this decline, we need to understand how human activities impact biodiversity, especially under future climate and societal change. Within this project, we aim to develop decision support tools based on state-of-the-art process-based models combined with AI techniques such as emulation and history matching.

These approaches allow us to deal with unprecedented levels of computational complexity, enabling us to:

1. model how individual species respond to climate and human-induced stressors with fine-grained detail at very large scales,
2. rigorously quantify uncertainties, and
3. dynamically re-calibrate models as new data emerges.

By advancing these cutting-edge approaches, our goal is to provide decision-makers and land managers with robust, fast running decision support tools that allow for the systematic exploration of pathways to halt biodiversity loss and promote nature recovery.

Bending the curve of biodiversity loss requires an integrated approach that combines the refinement of the understanding of the processes governing biodiversity change from a natural standpoint, as well as the impacts of human activities on nature within evolving societal and policy contexts. We seek projects that address either or both themes, emphasizing the development of innovative tools that integrate existing or novel process-based biodiversity models with advanced AI techniques. These techniques may include model emulation using deep Gaussian processes, network emulation, or history matching and calibration methods that explicitly quantify and propagate uncertainty from diverse sources.

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