I have two open positions for funded studentships at the University of Liverpool. Applications are open until 10 January 2020.
Follow the links for more detailed versions.
The goal of this project is to investigate the use of existing machine learning techniques to process gravity and magnetics data using the Equivalent Layer Method. The methods and software developed during this project can be applied to process large amounts of gravity and magnetics data, including airborne and satellite surveys, and produce data products that can enable further scientific investigations. Examples of such data products include global gravity gradient grids from GOCE satellite measurements, regional magnetic grids for the UK, gravity grids for the Moon and Mars, etc.
The goal of this project is to develop improved inversion methods to determine crustal thickness from gravity and gravity gradient data, in particular Uieda and Barbosa (2017). Main objectives are: (1) account for density variation in the oceanic lithosphere due to temperature; (2) incorporate seismological estimates of crustal thickness in the inversion process; (3) estimate the density contrast across the crust-mantle interface in different domains; (4) joint inversion of gravity and gravity gradient data; (5) develop techniques to reduce the computational load of the inversion; (6) quantify uncertainty due to errors in regional crustal and sedimentary basin models. The inversion methods developed in this project can be applied to produce improved crustal thickness estimates for South America, Africa, Antarctica, the Moon, Mars, etc.
The funding for these projects comes from the School of Environment Sciences. Applicants choose a project when applying and will be judged on their own merit (not the project/supervisor). There are only a small number of studentships available for the entire School, so competition for the studentships tends to be high. Sadly, applications are limited to UK and EU citizens. Candidates who are able to self-fund (e.g., through their employer) are encouraged to apply as well. In this case, there is no need to go through the normal competition.
Both projects have a large computational component. Students will make code contributions to the different open-source Python software developed by the lab. They will be trained to develop software in a collaborative environment using GitHub and use the current best practices in software engineering and reproducible research.
Applicants are encouraged to read the lab manual to familiarize themselves with the way we approach science, expectations, our code of conduct, etc.
If you're interested in applying (or know someone who might be), please get in touch!