Postdoctoral Research Associate in Paleoclimate Statistics and Dynamics

Postdoctoral Research Associate in Paleoclimate Statistics and Dynamics

University of Cambridge

Cambridge, United Kingdom

Applications are invited for a Postdoctoral Research Associate position for a fixed term of 36 months to work on the ERC Synergy Grant FORAGER. The post will be based in the Department of Geography at the University of Cambridge and will work under the guidance of Dr Matt Osman.

The primary expected deliverable of the post will be the development of an updated generation of paleoclimate reanalyses spanning the Last Ice Age to the present, building on recent products developed by the C3PO research group. The role will entail synthesising and pairing globally distributed paleoclimate proxy records (e.g. ice core geochemistry, terrestrial and marine biomarkers, speleothems) with ensembles of climate model simulations using probabilistic methods, in order to produce dynamically consistent, spatially complete, and observationally coherent climate fields at decadal to centennial resolution. Emphasis will be placed on uncertainty analytics, proxy system emulation, and the development of statistical spatiotemporal downscaling approaches that help to improve the gap between native climate-model resolution and the types of sub-regional- or seasonal- scales required by end-users.

This role will deliver a core explanatory component of the wider FORAGER project and will involve collaboration across a large international and interdisciplinary consortium. Beyond this, the position offers scope for the post-holder to develop an independent research programme including work on Bayesian model-proxy comparison, modelling, probabilistic model weighting, or other aspects of climate/paleoclimate dynamics aligned with the broader ongoing research interests of the C3PO group.

Applicants should hold a PhD (or be close to completion) in climate science, Earth system science, applied mathematics, physics, statistics, or a closely aligned discipline, and have strong quantitative and scientific programming skills. Experience with Bayesian statistics, paleoclimate data, climate modelling, and (or) data assimilation is desirable. The candidate must be highly motivated and have excellent time management, organisation, communication, and teamwork skills.