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Postgraduate research project

Understanding ionospheric dynamics with machine learning

Fully funded (UK and international)
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

The Earth’s magnetosphere-ionosphere (M-I) system is a highly dynamic plasma environment which is driven by its interaction with the solar wind through a process called “reconnection”. Understanding the M-I system is important, as it provides the scientific basis of space weather (the harmful influence of our plasma environment on space- and ground-based technology).

Ionospheric observations of reconnection are uniquely capable of inferring the global extent, and hence global rate, of the reconnection process. They therefore hold the key to understanding how the global M-I system responds to solar wind driving. However, there are some key unknowns – in particular, fundamental dependencies such as how the upstream conditions control the spatial extent of the interaction process or the “size” of individual reconnection bursts are not known. Our recent work [1] opens up an exciting opportunity to probe these questions. However, to do so requires large-scale statistical studies, and the challenge is one of data volume. This is a challenge that can be addressed with data science techniques [e.g. 2]. In this project, we will develop automated algorithms to identify reconnection events based on ionospheric radar data, which will allow transformative statistical studies into the nature of M-I driving.

For full project details visit the Inspire project page.


  • Professor Robert Fear (University of Southampton)
  • Professor Jonathon Hare (University of Southampton)
  • Professor Adam Prügel-Bennett (University of Southampton)
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