University of York

University of York

Master of Science by Research

The Master of Science by Research in Computer and Information Systems is offered by University of York.

Program Length: 2 YEARS.

Master of Science by Research offered by the University of York

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

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). They 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.

MSc by Research projects
They are currently recruiting MSc by Research students. They encourage you to do some research on their 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.

They have many exciting  projects on offer within their five research areas. A selection are given here, new opportunities come up all the time so please do get in touch. They'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. 

How artificial intelligence can improve nuclear physics?
 In recent years, thanks to the remarkable progress in the domain of artificial intelligence, they have now atour disposal very powerful tools to help their scientific investigation. Neural Networks or Gaussian Process Emulators (GPE) are now used to reduce the computational cost of complex numerical codes, 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 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): 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, they 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 their scientific investigations.



Locations where you can study Master of Science by Research en University of York


Schools where you can study Master of Science by Research en University of York

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