Application deadline: Wednesday, January 06, 2027
Funding: Self-Funded PhD Students Only
About the Project
Supervisors: Professor Poshak Gandhi and Professor Christian Knigge
Accurate stellar distances underpin our understanding of the Galaxy. This project will develop a neural network that combines diverse astronomical observations to estimate distances, even when Gaia parallaxes are uncertain or missing, and improve existing estimates. It will advance stellar astrophysics while developing machine learning methods with applications beyond astronomy.
The Gaia mission has recently provided photometric (brightness) and astrometric (positional) information for nearly 2 billion astronomical objects. Perhaps most importantly, it has provided parallaxes -- and hence allowed distance estimates -- for roughly 1.5 billion sources. However, only about 10% of these have "good" parallaxes sufficient for inferring distances robustly.
In this project, we will develop, train and exploit a machine learning algorithm that will provide useful distances estimate even for sources with highly uncertain Gaia parallaxes -- or perhaps no parallaxes at all. At the same time, it will also improve the quality of the distance estimate for sources that do have good-quality parallaxes.
The core idea is that almost every astronomical measurement contains at least some information about the distance to that source. For example, distant sources will appear fainter, whereas sources located behind dense interstellar clouds may be closer than they appear.
We will develop and train a neural network combines source observations to provide the best possible distance estimate for the source. Machine-learning algorithms that can work with different kinds of data simultaneously are known as "multi-modal", and they are extremely powerful tools for extracting critical information hidden in complex data sets.
The specific distance-estimation tool we aim to develop will be immensely valuable to stellar astrophysics. The machine learning methodology we will learn about, implement and exploit has an even wider range of applicability, well beyond the astronomical context.
Entry requirements
A UK 2:1 honours degree, or its international equivalent.
Experience with machine learning is highly desirable.
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Physics (7089)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, poshak.gandhi@soton.ac.uk.
The School of Physics and Astronomy is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.