Accessibility statement

MSc by Research

The MSc by Research is a one-year programme which allows you to pursue your research interests.

Unlike a conventional taught Masters course, you work full-time on your own research project. You'll be supported with a small number of taught modules to develop your knowledge and practical skills.

You'll have 12 months to experiment and collect data, and a further three months (if required) to write your thesis.

The MSc course is ideal if:

  • You would like to study for a research degree without the three or four year commitment to a PhD degree
  • You would like to study a further qualification to prepare you for PhD study
  • You are an overseas student requiring an interim qualification after 12 to 15 months' study

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MSc by Research - Distance Learning

The MSc by Research - Distance Learning (DL) - is a one-year programme which allows you to pursue your research interests from home.

The DL learning outcomes are the same as those of the MScR above, as are the entry requirements (see further below). We will, however, ask for justification for taking the DL option and will then assess the feasibility of the work programme. The latter will be embodied in a plan of study outlining resources and facilities needed, training requirements and an associated timeline, which will need to be in place before being accepted onto the programme. A research topic that requires specialised equipment and facilities may not be possible as DL, unless special arrangements can be agreed in advance which ensure that these needs can be met for the duration of your degree.

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Find a supervisor

We encourage you to identify a potential supervisor whose expertise would benefit your research.

Most of our academics accept applications for self-funded MSc study all year round.

Progress to PhD

This degree is closely aligned to the first year of the PhD programme. You may transfer to the second year of a PhD after your course, subject to satisfactory progress, funding and available supervision.

MSc by Research projects

We are currently recruiting MSc by Research students. We encourage you to do some research on our academics to really get to know how their work and expertise fits with your interests before you apply. If you wish to learn more about a particular academic's research or discuss a project you have in mind, they are happy to answer specific questions by email or telephone.

We have many exciting  projects on offer within our five research areas. A selection are given here, new opportunities come up all the time so please do get in touch. We're always happy to hear proposals for new research projects. If you have something in mind and can't find your perfect project listed below, just identify a potential supervisor and get in touch. 

Find a supervisor

How artificial intelligence can improve nuclear physics?

 In recent years, thanks to the remarkable progress in the domain of artificial intelligence, we have now atour disposal very powerful tools to help our scientific investigation. Neural Networks or Gaussian Process Emulators (GPE) are now used to reduce the computational cost of complex numerical codes [1], but contrary to a simple interpolation, they are able to grasp the underlying physics using a very reduced set of hypothesis. The York Nuclear Physics group has started investigating the possible usage of machine learning (ML) methods in 2017 [2] by applying a GPE to simulate the structure of an inner crust of a neutron star. By reducing the typical computational cost by several orders of magnitude, the ML opens up completely new line of research. At present Dr. Barton and Dr. Pastore are working on the development of a new nuclear mass model based on neural networks (NN) [3]: the preliminary results show that a NN can help reduce the typical discrepancy model/observation by roughly a factor of 3. The resulting masses have now a reasonably low error bar and thus they can be used in astrophysical scenarios as supernovae explosions. The candidate will work on several ML algorithms using a data-driven approach. During the 3-year project, we will study the possible implementation of several ML methods to various theoretical, experimental, and application problems. This may include improving model accuracy, reducing computational cost, applying Bayesian analysis to improve the accuracy of extrapolations, automating nuclear detector calibrations, and improving the position resolution and performance of nuclear detectors. There will by synergies within the whole of the nuclear physics group since these ML tools will aid our scientific investigations.

[1] Regnier, D., Lasseri, R. D., Ebran, J. P., & Penon, A. (2019). Taming nuclear complexity with a committee of deep neural networks. arXiv preprint arXiv:1910.04132.
[2] Pastore, A., Shelley, M., Baroni, S., & Diget, C. A. (2017). A new statistical method for the structure of the inner crust of neutron stars. Journal of Physics G: Nuclear and Particle Physics, 44(9),
[3] D. Neil, K. Medler, A. Pastore, C. Barton (in preparation)

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Apply now MSc by Research - Distance Learning


Our specialist facilities give you access to cutting-edge technologies and powerful equipment. On top of this, we provide access to a fully-equipped workshop with support and training, to help you design and build bespoke experimental tools.

Entry requirements

You should have, or be about to complete, a BSc degree at 2:2 (or equivalent) in Physics, or a related subject.

English language requirements

How to apply

You can apply online. You will need to provide:

  • a CV
  • an official academic transcript (or interim document if you have not yet received your award)
  • contact details of two academic referees

On your application form please let us know about your research interests. If you are interested in working with a particular supervisor, please make this clear.

Apply now MSc by Research

Apply now MSc by Research - Distance Learning

Contact us

Dr Stefanos Paschalis

Graduate Admissions Tutor

Mandie Hudson

Postgraduate Admissions Administrator

Fees and funding

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