About the project or challenge area

New machine learning methods provide a great opportunity to improve our weather and climate prediction capability, through deriving representations of the highly complex processes underlying weather events from data. Areas of particular focus in our group are downscaling (making detailed high-resolution predictions based on coarse-resolution weather and climate model outputs) and improving weather prediction. We place a high importance on correctly capturing extreme events that cause the greatest impacts. This is a fast-changing field and new project opportunities are always coming up, so please get in contact to find out what is currently available. Examples of student projects that we have run/are running include predicting high-resolution hurricane rainfall from coarse-resolution data, developing an efficient emulator of the Met Office’s high-resolution UK rainfall model, and improving African weather forecasts, all based on the latest advances in neural networks. Future projects can be tailored to students’ interests, experience and abilities.

Why choose this project?

You will have the opportunity to learn how to analyse weather and climate simulations and to train machine learning algorithms to produce a scientifically useful tool. This will give you very valuable expertise in both weather and climate science and AI. We would also expect there to be involvement with external partners that we work with, including the Met Office and other universities, providing an opportunity to make further links with the community of researchers and users. 

About you

You will have enthusiasm for developing scientifically useful machine learning applications and understanding high-impact weather events. The project would also best suit a student with experience of writing computer programs to carry out data analysis. Prior experience of machine learning is not essential, but would make an application more competitive.

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.