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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training




Themes and Techniques

Luke Abraham Yusuf Hamied Dept of Chemistry Atmospheric chemistry, climate , interactions and earth-system modelling (weather, climate) 
Alex Archibald Yusuf Hamied Dept of Chemistry Atmospheric chemistry, biosphere-atmosphere, volatile organic compound (air quality), modelling
Mike Bithell Department of Geography, Centre for Science and Policy, Cambridge Centre for Data-Driven Discovery                                                  Numerical modelling of spatially distributed systems
Carl Henrik Ek Dept of Computer Science and Technology Uncertainty quantification, bayesian non-parametrics, active learning, approximate Inference (climate)
Hamza Fawzi Dept of Applied Math and Theoretical Physics Convex optimisation and applications
Jennifer Gabrys Department of Sociology  Air quality, forests, digital social research, interviews, participation
Chiara Giorio Yusuf Hamied Dept of Chemistry xploring the present and past of the earth's atmosphere using advanced analytical tools 
Michael Herzog Dept of Geography Development of atmospheric models from local to global scales, modelling of convective clouds and plumes, role of convection in the climate system, understanding of the hydrological cycle, understanding of the role of aerosols, impact of aerosols on dynamical and microphysical processes
Mateja Jamnik Department of Computer Science and Technology Artificial intelligence, reasoning and machine learning, explainability (climate)
Ali Mashayek Department of Earth Sciences Climate Dynamics, Geophysical Fluid Dynamics, Marine Ecosystems, Data Science
Tracy Moffat-Griffin British Antarctic Survey Space, polar science, weather
Carl Rasmussen Dept of Engineering Inference and learning in non-parametric models, and their application to problems in non-linear adaptive control
Carola-Bibiane Schönlieb Dept of Applied Math and Theoretical Physics Nonlinear PDEs, inverse problems in imaging, sparse regularisation, machine learning for inverse problems
Emily Shuckburgh Dept of Computer Science and Technology Communication and public attitudes to climate change and climate science, linking climate change and sustainability, improving predictions of future climate change using theoretical approaches, observational studies and numerical modelling, transport and dynamics of the atmosphere, oceans and climate, role of the polar oceans in the global climate system, artificial intelligence, data science, machine learning
Koen Steemers Dept of Architecture Air quality, biodiversity, buildings, energy, data science, modelling
Liz Thomas British Antarctic Survey Climate, polar science, modelling
Richard Turner Department of Engineering Machine learning, computer perception, statistical signal processing, machine learning for climate science
Damon Wischik Dept of Computer Science and Technology

Visualisation (programming languages), simulation (climate)

Eiko Yoneki Dept of Computer Science and Technology Research methods, social networks, social media, cryptography, simulation, uncertainty quantification