MSc by Research

We have an active research masters programme for self-funded students. Masters students carry out an independent research project with support from a member of academic staff, which is assessed on the basis of the examination of a thesis after one year of research (part time study is also possible). Students are expected to attend advanced undergraduate and postgraduate lecture courses in relevant subjects, and follow courses of directed reading, but the emphasis of the programme is on research rather than the taught element.

A limited number of University MSc studentships of £5k are available for the MSc programme, but applicants must provide the remaining funding. For information on fees and funding, please visit the Postgraduate Study - Fees and Funding page, see also the Postgraduate Study pages.

To apply for an MSc, please use our online application form, and select “Physics (MSc by research)” as the programme. At the top of your personal statement, please state clearly that you are applying for an MSc by research in astrophysics, and state which of our research areas you are interested in (you may list as many as you like). The official deadline for applications is 19 January 2026, but we will accept applications until the place is filled, and expect shortlisting to occur around Easter. Please send us an email if you have any questions.

Listed below are MSc projects available this year. Please feel free to contact the associated academic if you have questions about the projects. If you have a specific area of interest, and it aligns with the research undertaken by a member of staff, please feel free to contact that staff member directly to express your interest.

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Project 1: Measuring the UV-optical spectrum of an exoplanet’s atmosphere (Supervisor: Dr Hannah Wakeford)

There are over 6,000 known exoplanets, most of them discovered through the transit method, viewing the planet as it passes in front of the star via the reduction in stellar light measured. Transits also enable us to measure the atmospheres of these worlds using spectroscopy, breaking down the transit time series light curve into different wavelengths to discern absorption signatures in the planet’s spectrum. Using observations from space-based observatories we will extract and analyse the spectrum of a hot gas giant exoplanet from the UV to optical, assess the potential of contamination from nearby stellar companions, and determine the properties of the planet's atmosphere. You will be working directly with the data leading the analysis as part of an international team. This project will require good coding skills (e.g. Python or IDL) and understanding of uncertainties, or enthusiasm to learn them.

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Project 2: Using machine learning to explore galaxy cluster scaling relations (Supervisor: Prof. Ben Maughan)

Galaxy clusters are massive cosmic objects consisting of 100s - 1,000s of galaxies, an atmosphere of X-ray emitting plasma and dark matter (the latter of which accounts for about 85% of their mass content). The observed properties of the gas and galaxies in clusters correlate strongly with their total mass, in what are known as "scaling relations". Measurements of these scaling relations can be used to probe the physical processes that shape the properties of the gas and galaxies, and also to provide a tool to estimate cluster masses from easy to observe properties, such as the luminosity or temperature of the cluster gas. Measuring these scaling relations is difficult because the data are affected by selection biases and noise that can be difficult to model statistically. In this project you will investigate a machine-learning and statistical technique called Simulation-Based Inference (SBI) as a tool for measuring these scaling relations. The project will be based around developing and validating SBI methods on simulated data - if successful then the new methods may be applied to real data to make new measurements of the galaxy cluster scaling relations. The project will require strong Python coding skills, and enthusiasm to learn advanced statistical techniques for data analysis.