ACAD107854

You will be responsible for combining a range of different satellite and ground-based observations of glacier mass balance to produce a time series of global mass balance from the 1970s to the present day. In addition, they will help in the development of the Open Global Glacier Model (OGGM) to improve its representation of current mass balance and consequently future projections. The aim of the observational combination is to use a Bayesian Hierarchical Modelling approach that accounts for the uncertainties in the different data sets.

A background in statistical inference and/or signal processing applied to an environmental problem and/or numerical modelling experience would be advantageous. Applicants should have strong statistical and computational skills, ideally related to problems in geoscience. Some glaciological background would be advantageous but not essential.

What will you be doing?

You will be processing and analysing a variety of global and regional glacier mass balance time series derived from different satellite methods including stereo-photogrammetry, gravimetry, altimetry as well as in-situ data collected in the field. You will use state of the art statistical inference methods to combine the data in a way that accounts for their uncertainties and the different sampling in space and time. You will also contribute to the development of the glacier model OGGM to improve its representation of present-day mass balance and future projections, which is written in Python as an open-source community resource hosted on GitHub. You will be working with an international team of scientists across Europe and India.

You have strong numerical and computational skills acquired in a field of geoscience. You are excited by contributing to important problems in climate science and have good inter-personal skills and enjoy working in a dynamic team.