We have now completed our recruitment for October 2025. This process involved over 300 applications and more than 50 interviews.
15 outstanding students will be joining UNRISK this year, of which 5 are international.
6 students – Leeds
5 students – UCL
4 students – Exeter
The projects selected are below. View details at https://unrisk-cdt.ac.uk/projects/
| Physics-informed Machine Learning for decision making related to future extreme weather events |
| Novel statistical AI approaches for modelling and evaluating extreme windstorm risk |
| Leveraging probabilistic AI for heat-related health risk mitigation and adaptation |
| Advanced Uncertainty Quantification for decision support: Balancing risks and preferences for policy decisions |
| Protecting UK infrastructure from landslides triggered by climate extremes |
| Predicting biodiversity loss in mountain rivers due to glacier retreat |
| Quantifying the role of land cover in ‘slowing the flow’ for flood risk reduction in a changing climate |
| The influence of physical process representations on regional and global-scale climate model output |
| Using new high-resolution ensembles to quantify uncertainty in projections of African climate processes |
| Reducing uncertainty in the effect of clouds on climate change |
| End-to-end Machine Learning quantification of hydrological uncertainties: from climate to flood risk management |
| Leveraging Machine Learning algorithms for improved Arctic sea-ice prediction using the Met Office suite of models |
| Inclusive storylines for sustainable governance of urban adaptation to uncertainties in future heat extremes |
| Reducing uncertainty in climate risk perception to enhance resilience: A data science approach using the World Risk Poll |
| Combining models and uncertainties to support flood risk assessment and mitigation strategies |
