Research Fellow in Statistical Emulation of Climate and Ice Sheet Models

University of Leeds

Leeds, UK

33,199 to £39,609

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Are you an ambitious statistician looking for your next challenge? Do you want to work with world leaders in glaciology and artificial intelligence to tackle future sea level rise? Do you want to further your career in one of the UKs leading research intensive Universities?

You will join a team of climate scientists, glaciologists and statisticians led by Dr Lauren Gregoire as part of the prestigious UKRI Future Leaders Fellowship project “Constraining projections of ice sheet instabilities and future sea level rise”. The ambitious and exciting aim of the project is to provide robust estimates of ‘worst case’ future sea level rise using information from past events when ice sheets became unstable. You will develop SMB-Gen, the first ‘emulator’ of past climate and ice sheet surface mass balance using cutting-edge techniques in Uncertainty Quantification and Artificial Intelligence. This emulator will be used to simulate past and future instabilities in the Greenland, Antarctic and Northern Hemisphere ice sheets and the resulting sea level rise. There is a possibility of extending this employment as new avenues for research emerge. You will work with partners from the MetOffice, the Environment Agency and energy and transport industries on the co-production of knowledge on ‘worst-case’ sea level rise. You will be co-supervised by Danny Williamson, Fellow of the Alan Turing Institute and Senior Lecturer at the University of Exeter and will have opportunities for extended visits / secondments to project partners.

You will have, or be close to obtaining, a PhD in Statistics, Machine Learning or a related subject, ideally with expertise in Bayesian optimisation, Gaussian process emulation or Uncertainty Quantification. Experience in applying these techniques in environmental sciences or a familiarity with climatology or glaciology is also desirable. You will have evidence of a strong commitment to publishing and presenting scientific results at an international level and experience in working as part of a team.


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