About the project or challenge area

This project is a follow-on from the ERC grant GlobalMass (www.globalmass.eu) that has advanced the use of space-time statistical inference to separate global sea level rise into its different sources. We have developed a software package called 4DModeller (see https://4dmodeller.github.io/fdmr/) that is designed to tackle a wide range of spatial-temporal problems. In this project we want to apply 4DM to the global glacier mass balance data sets that are available from in-situ and satellite data. These data sets vary in their length of record, coverage and quality and 4DM is designed to deal with exactly this kind of problem. The aim will be to combine the data sets to produce, for the first time, a consistent time series of glacier mass balance worldwide and to correlate this with the forcing climate that influences their behaviour. This will allow us to then predict their behaviour using climate forecasts from GCMs.

Why choose this project?

The student will be working with experts in climate science, glaciology, and machine learning. They will be co-supervised by an industrial partner, Expert Analytics, so this is a great opportunity to see work within and beyond academia.

About you

The student would benefit from having programming experience, preferably in R, but other languages such as Python would give the grounding needed. A background in Physics, Electrical Engineering, signal processing, or statistics would be advantageous.

How to apply

All students can apply using the button below, following the Admissions Statement (PDF, 188kB). Please note that this is an advertised project, which means you only have to complete Section A of the Research Statement.

This project is not funded, for further details please use this link.

Before applying, we recommend getting in touch with the project's supervisors. If you are interested in this project and would like to learn more about the research you will be undertaking, please use the contact details on this page.