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Seeing into the subsurface – next generation geophysical imaging for environmental and engineering hazard monitoring

Are you looking to transfer your experience of computational methods to tackling some important Earth science problems?   We are interested in imaging the shallow subsurface (the top 100m of the Earth’s crust – the bit we rely on for water and many other resources and the bit that can change rapidly, e.g. a landslide, and have a huge impact on society).  We are looking for a graduate with quantitative skills to help develop a new generation of 4D (space and time) geophysical simulators to allow us to image the subsurface at new scales.     Geophysical techniques now offer the potential to image complex large scale subsurface structures and processes, helping us improve our understanding of, for example, landslides, volcanoes, thawing of permafrost, groundwater contamination, and consequently the threats they pose on society. We are, however, at present constrained by the size of problem we can investigate with such techniques because of available computational power and computing approaches.

You will explore the potential of emerging computational approaches to transform our ability to image the Earth’s subsurface and quantify the model uncertainty.  We envisage a new generation geophysical simulator, that may be based on machine learning approaches, allowing us to study the Earth’s dynamic subsurface at a scale that is an order of magnitude above what we can presently do.   Once developed, you will test the new approach on rich datasets such as those obtained from monitoring landslides and earth dams.

You will work within a team from the Lancaster Environment Centre (LEC), British Geological Survey (BGS), UK Centre for Ecology & Hydrology (UKCEH) and Nottingham University, with CASE industrial partner Socotec. You will have access to state-of-the-art measurement systems (and their data) along with existing software used to analyse such data.


Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent. They should have studied to degree level subjects such as Computer Science, Applied Mathematics, Computational Physics, Engineering, or Earth Science with strong numerical elements.


For further details please contact Prof Andrew Binley (