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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training


Student Department Topic area PhD project title
Herbie Bradley Yusuf Hamied Dept of Chemistry Climate Chemical mechanism emulation in climate model simulations for exascale supercomputing applications
Luke Cullen Dept of Engineering Climate Reducing emissions uncertainties using data fusion within graph representations
Seb Hickman Yusuf Hamied Dept of Chemistry Air quality, climate, environmental hazards Determining the risks and drivers of extreme ozone events during heatwaves with machine learning and causal inference
Yilin Li Yusuf Hamied Dept of Chemistry Air quality Using advanced sensor technologies, detailed health outcomes and AI techniques to investigate the underlying mechanisms of air pollution on health
Raghul Parthipan British Antarctic Survey and Dept of Computer Science and Technology Climate Probabilistic machine learning, using GANs and Gaussian Processes, to reduce climate model computational costs, improve parameterisations and improve accuracy
Ira Shokar Dept of Applied Math and Theoretical Physics Climate Data-Driven Exploration of Parameterisation Schemes within Models of the Tropospheric Mid-Latitudes
Tudor Suciu Dept of Computer Science and Technology Climate AI for Climate related Business Risks – Partner WTW
Kenza Tazi British Antarctic Survey and Dept ofEngineering Climate Local alpine precipitation predictions from largescale data: a probabilistic machine learning approach
Sophie Turner Yusuf Hamied Dept of Chemistry Climate Optimising Atmospheric Photolysis Simulations for Climate Models
Anna Vaughan Dept of Computer Science and Technology Weather, environmental hazards On-board physics informed learning for severe weather nowcasting
Mala Virdee Dept of Computer Science and Technology Climate AI for climate risks in sustainable development
Michelle Wan Yusuf Hamied Dept of Chemistry Air quality Machine learning applications to the study of air pollution exposure & human health