The British Antarctic Survey (BAS) is looking to recruit an Ice Sheet Modeller to join our research programme focused on addressing deep uncertainty in future ice loss from Antarctica. Our overarching goal is to improve our understanding of ice dynamics in order to reduce uncertainties in projections of Antarctica’s sea-level contribution over policy-relevant timescales (from the present day to the coming few centuries).

We are looking for someone whose proposed research will enhance BAS’s capability to deliver on the big questions facing society over the coming decades and centuries: how much and how fast will the Antarctic Ice Sheet contribute to global sea level rise? This post is aligned with a strategic initiative to deliver on the new BAS Science Strategy. There will be significant scope to tailor the position to the candidate’s interests, aligned with the interests of the research community at BAS. Research interests of the group include, but are not limited to, modelling ice-ocean or ice-atmosphere interactions, machine learning/emulation for projections, combining observations and models, and modelling dynamic ice-sheet processes (e.g. ice fracture). An enthusiasm for integrating machine learning, emulation, and/or optimal experimental design (active learning) techniques into ice sheet modelling and sea level rise science is desirable. Specific experience of using machine learning in a glaciological context is not essential.

The successful candidate will join the Ice Dynamics and Palaeoclimate Team at BAS, which includes ice sheet modellers, ice core scientists, Earth system modellers and geophysicists. BAS is an interdisciplinary and collegiate organization and strong collaborations between science teams are core to our work; the successful post-holder will have opportunities to build on and develop collaborations across BAS. Support and training will be provided to enable the successful candidate to make use of the opportunities BAS has to offer and to develop their scientific career.

The start date for this role is flexible, but ideally autumn/winter 2024. If this role appeals to you, we’d love to hear from you, whatever your background. Informal enquiries about this post are very welcome and should be addressed to Rosie Williams (This email address is being protected from spambots. You need JavaScript enabled to view it.).