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PhD in Astronomy and Machine Learning: Improving Gaia Parallaxes with Multi-Modal Neural Networks University of Southampton in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Competition funded PhD project.
Deadline
Jan 6, 2027
Country
United Kingdom
University
University of Southampton

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About this position
Postgraduate research project at the University of Southampton in the Faculty of Engineering and Physical Sciences.
Project title: More data, more and better distances: Improving on Gaia parallaxes with multi-modal machine learning.
This PhD project sits at the intersection of astronomy, physics, machine learning, neural networks, and statistics. The research aims to improve stellar distance estimates by combining diverse astronomical observations, especially when Gaia parallaxes are uncertain or missing. The project will develop and train a multi-modal neural network to infer distances more robustly and to improve existing estimates for sources with good-quality parallaxes as well.
The scientific motivation is strong: accurate stellar distances are fundamental for understanding the Galaxy and for broader stellar astrophysics. The methodology is also transferable beyond astronomy, making this a good fit for applicants interested in data-driven methods and quantitative research.
Funding: Competition funded.
Entry requirements: A 2:1 honours degree is required.
Deadline: 6 January 2027.
How to apply: Use the University of Southampton postgraduate research application route for this project. Review the full entry requirements and funding details on the project page before submitting your application.
Funding details
Competition funded PhD project.
What's required
Applicants should have a 2:1 honours degree. The project is in the Faculty of Engineering and Physical Sciences and is suitable for candidates with interest in astronomy, astrophysics, and machine learning.
How to apply
Review the project details on the University of Southampton page and use the university's postgraduate research application process to apply before the closing date. Check the full entry requirements and funding information on the project page.
More information can be found here
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