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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training
 

Academic Staff

                                             Department/Affiliation Themes and Techniques
David Coomes Department of Plant Sciences Agriculture, biodiversity, environmental hazards, forest, space, artificial intelligence, data science, machine learning, modelling, remote sensing
Nik Cunniffe Department of Plant Sciences Theoretical and computational epidemiology (forests), modelling
Jennifer Gabrys Department of Sociology  Air quality, forests, digital social research, interviews, participation
Theo Hacking Cambridge Institute for Sustainability Leadership Decision support tools for sustainability/ sustainable development, including social and environmental impact assessment and sustainability assessment
Richard Harrison Department of Earth Sciences Mineral sciences, nanopaleomagnetism (natural resources)
Marian Holness Department of Earth Sciences Petrology: Igneous, Metamorphic and Volcanic studies (natural resources), petrographic techniques coupled with geochemical analysis to decode rock history
Srinivasan Keshav Department of Computer Science and Technology Buildings, energy, forests, artificial intelligence, data science, machine learning, modelling, remote sensing
Emily Lines Department of Geography Forests, remote sensing
Adam Pellegrini Department of Plant Sciences Forests, environmental hazards and disaster risk, modelling
Emilie Ringe Department of Earth Sciences Multi-Scale, multi-dimensional imaging of natural and synthetic materials (natural resources)
Nina Seega Cambridge Institute for Sustainability Leadership, Centre for Science and Policy Financial risk management, sustainable finance
Andrew Tanentzap Department of Plant Sciences Natural resources, artificial intelligence, data science, machine learning, modelling, remote sensing
Adrian Weller Department of Engineering, Centre for Science and Policy, Alan Turing Institute Machine learning and artificial intelligence, their applications and their implications for society