PhD Studentship: Space Trajectory Classification using Machine Learning

4 weeks ago


Southampton, United Kingdom University of Southampton Full time

PhD Supervisor: Dr Alexander Wittig
Project description:
Modern trajectory design aims to identify and exploit features in the natural dynamics to design trajectories meeting mission requirements. GPU parallelization allows generating large trajectory databases for a wide range of initial conditions.
In this project you will apply Machine Learning techniques to this data to extract the essential information contained within these trajectories.
Develop tools to efficiently generate a large database of relevant trajectories using state-of-the-art GPU technology for astrodynamics systems of interest.
# Investigate and apply suitable Machine Learning algorithms for unsupervised classification of similar trajectories and automatic extraction of their relevant features.
# To apply for this project, you need a solid background in the following:
computer programming (in a compiled language such as C, C++, Fortran, or Julia)
machine learning and/or artificial intelligence
big data analysis and handling (HPC)
This project is supervised by Dr Alexander Wittig and hosted at the University of Southampton with funding from the US Airforce Office of Scientific Research and will be co-supervised by Dr Davide Amato from Imperial College London. Funding for travel between these institutions will be made available.
Please note that funding is only available for UK and EU students (countries within Horizon Europe).
If you wish to discuss any details of the project informally, please contact Alexander Wittig, Astronautics Research Group, Email: a.wittig@soton.A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton Apply early for the best opportunity to be considered.
Apply online, by clicking the 'Apply' button, above.Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships
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