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I started a PhD at Ïã¸ÛÁùºÏ²Ê funded by the CDT DIS program within the Physics and Astronomy departmentÌýin collaboration with the Space Geodesy and the Atmospheric Sciences research labs at Ïã¸ÛÁùºÏ²Ê.

Project title: Unveiling Atmospheric and Orbital responses to Space Weather phenomena with Machine Learning and Data assimilation for Satellite Orbit prediction and traffic management

Research Group:ÌýAstrophysics

Supervisor(s): ProfÌýAnasuya Aruliah

Introduction:Ìý

I have a background in Theoretical Physics as well as Earth and Planetary Sciences. I hold a Master’s degree from Ïã¸ÛÁùºÏ²Ê in Remote Sensing and Environmental Mapping, for which I produced an interesting piece of research on mapping the Sea Ice albedo over both polar regions using satellite imaging. I was subsequently invited to present my research at multiple conferences in Europe, as well as at NASA Jet Propulsion Laboratory and Caltech University in Los Angeles.

I have also completed finance internships at UBS and JPMorgan, where I learned to manage investment portfolios. More recently, I took a course on Impact Investment at Harvard Kennedy School.

In September 2023, I started a PhD at Ïã¸ÛÁùºÏ²Ê funded by the CDT DIS program within the Physics and Astronomy departmentÌýin collaboration with the Space Geodesy and the Atmospheric Sciences research labs at Ïã¸ÛÁùºÏ²Ê. My PhD focuses on using Machine Learning to understand the impact of solar storms on the trajectories of satellites orbiting in Low Earth Orbit, a hot topic within the Space Tech industry.

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Project description:ÌýÌý

My project aims to help solve the drag coefficient problem caused by space weather on Low Earth Orbit satellites and space debris. To achieve this, I will use global atmospheric models in conjunction with ground data from instruments such as Fabry-Perot Interferometers and EISCAT-3D, as well as satellite data from GRACE accelerometry. These data sets will be used to refine empirical models of the thermosphere. I will employ machine learning and data mining tools to disentangle the effects of solar storms at varying heights within the thermosphere. This will impact our understanding of physical properties and orbital dynamics, ultimately leading to the creation of advanced satellite orbital dynamics prediction tools.

First year group project:ÌýPeak Ai - Deep Learning for Supply Chain Management

Placement:Ìý

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