About the Project
Earth’s magnetosphere-ionosphere-atmosphere system is driven by the solar wind and coupled through plasma dynamics and global electromagnetic fields. This coupled MIA system can be understood as a dynamic network of interacting parts. Distinct regions in space (e.g. radiation belts, plasma sheet, ionosphere, neutral atmosphere) are connected via the Earth’s magnetic field. Measurements of the dynamics in each region exist but these are often point measurements of dynamic processes that vary across space and time. A major challenge is to make sense of vast measurement databases and build an integrated understanding of the dynamics. Modern machine learning methods such as Graph Neural Networks can help us solve these problems to make sense of the data chaos.
You will use machine learning methods on different types of measurements (e.g. ground-based radar and magnetometer measurements) as well as measurements from spacecraft missions to infer the links between the electrodynamics of different regions in space surrounding Earth. Your PhD project will exploit link prediction and clustering of data to evaluate physical links between distinct regions in space, and other methods to understand our dynamic system.
The Lancaster Physics Department holds an Athena SWAN silver award and Institute of Physics JUNO Championship status and is strongly committed to fostering inclusion and diversity within its community.