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Dr Guy Paxman

Assistant Professor (Research)

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Assistant Professor (Research) in the Department of Geography  41925


  • 2019 – 2022: Postdoctoral Research Scientist, Lamont-Doherty Earth Observatory, New York, USA
  • 2015 – 2019: Ph.D., Durham University, UK
  • 2011 – 2015: MEarthSci - Earth Sciences, University of Oxford, UK
Research Groups
  • Sea Level, Ice and Climate
Research Overview

I am a glacial geophysicist and geomorphologist interested in subglacial landscapes, geology, and Earth structure in the polar regions, particularly Greenland and Antarctica. My research focuses on the interactions between solid Earth processes, topography, and ice sheet dynamics, with the goal of understanding more about the behaviour of past, present and future ice sheets in warmer climates. This involves combined analysis of large geophysical and geological datasets, including radar-derived ice thickness and bed topography, gravity and magnetic anomalies, bedrock geology, crustal and lithospheric properties, offshore sediment records, and satellite remote sensing, alongside numerical modelling and machine learning techniques.

The focus of my Ph.D. was the reconstruction of palaeotopography in Antarctica over multi-million year time scales, and the impacts of landscape evolution on past ice sheet behaviour and stability. Since then, I have worked on a US National Science Foundation (NSF) funded project to predict coastal responses to a changing Greenland Ice Sheet. This included constraining past ice sheet extent and behaviour from geomorphological analysis of subglacial landscapes in the Greenlandic interior, improving models of solid Earth deformation across a range of timescales (i.e. elastic and viscous responses to ice sheet (un)loading), and developing projections of relative sea level and bathymetric change around the Greenland coastline in response to future warming scenarios.

My current research at Durham as a Leverhulme Early Career Research Fellow aims to develop a series of semi- and fully-automated methods of quantifying and classifying subglacial geomorphological features in Antarctica and Greenland. Use of machine-learning techniques will facilitate the analysis of large and hitherto under-utilised airborne geophysical datasets (e.g., radar, gravity, and magnetics) and help better classify the subglacial environment. The project also aims to combine landscape classification with dated offshore sediment records that constrain the timing and location of episodes of landscape modification. The overarching aim is to link landscape features to the process(es) responsible for their formation, and in turn better understand ice sheet extent and behaviour during past warmer climate intervals.


Journal Article