We expect to offer 10 fully-funded PhD studentships per year. Students with a strong background in computer science, mathematics, physics, or engineering are particularly encouraged to apply.
The CDT in Data Science is a 4-year programme at the University of Edinburgh: the first year provides Masters level training in the core areas of data science, along with a significant project. In years 2-4 students will carry out PhD research in data science, guided by PhD supervisors from within the centre.
There will be three application deadlines for PhD studentships beginning in September 2018:
* Applications after the mid-March 2018 deadline can still be considered if places are still available, but are not guaranteed full funding consideration.
The CDT in Data Science has two different types of studentships: a regular CDT studentship and a CDT studentship with a PhD project in collaboration with an industry partner. For a regular CDT studentship, students choose their PhD projects at the beginning of year 2 (after the MSc by Research); there are approximately 10 regular studentships available. For a PhD project in collaboration with an industry partner, students commit to a particular PhD project at the application stage.
The application deadline for the following Studentships was 17 March 2017:
* PhD studentship on Declarative Programming for Data Science - http://homepages.inf.ed.ac.uk/jcheney/group/dpds.html
* PhD studentship in Improving News Reader Experience with Online Journalism
* PhD studentship in Improving Two-step Prediction Models in Consumer Research
The CDT's Open Days are opportunities to learn more about the programme, as well as the University of Edinburgh. Join us for in-person or virtual presentations and informal sessions to ask questions and discuss research opportunities.
Due to constraints from the CDT in Data Science's funding agencies, there are different rules for funding depending on your residence eligibility status:
UK and EU students:
Full funding (fees and stipend) - available to students who have been ordinarily resident in the UK for a minimum of three years prior to application and normally with no restrictions on how long they can stay in the UK.
Fees only funding - available to students who have been ordinarily resident in a member state of the EU (excluding the UK), in the same way as UK students must be ordinarily resident in the UK.
See EPSRC (TGC6 'Student Eligibility') for full clarification on eligibility requirements.
Non-EU students: Funding is significantly more competitive; see more detailed information for non-EU students.
If you are unsure about which of these two categories applies, please read the University web pages about fees and fee status. Please also refer to EPSRC funding guidance for clarification on eligibility requirements.
Before applying, please read the admissions information at the following links:
All applicants should ensure that their references are aware that applicants are applying for the full 1 year MSc by Research + 3 year PhD programme at the University of Edinburgh. Reference letters should include information about an applicant’s ability to undertake a PhD as well as an MSc by Research. We have a web page directed to referees to alert them to this. You might wish to send them a link to that page.
Letters of reference should be signed, dated, and on letter-headed paper: www.ed.ac.uk/studying/postgraduate/applying/references
When you apply, you will be asked to include a personal statement statement where you can note your reasons for wishing to undertake PhD training in data science. This is separate from the research proposal.
A formal research proposal is required as part of the application process for the CDT in Data Science. In the research proposal you should indicate the area you believe would be of most interest to you for a PhD topic. If you are not familiar with the field, the proposal can be quite broad and your actual PhD project can differ from what you propose during your application. It is possible, and even likely, that you will change your interest during the course of the first year. Nevertheless, the motivation and research proposal are very important in our selection, so please pay attention to it! It is probably easier to attach a file (either via “Upload a research proposal” or “Upload an unspecified document”) rather than to enter it all on the web form.
Before writing your proposal, you should visit the Research and Supervisor pages of our website. Both of these should give you a background on areas of research as well as current and potential research projects for postgraduate students.
There is no specific guidance for research proposals for applications to the CDT in Data Science; however, you may find it useful to refer to this document about writing a postgraduate research proposal for the University of Edinburgh.
Please include a CV or résumé with your application. You should upload this as a supporting document in EUCLID.
Please provide a statement of your preferred research area if you can (e.g. Machine Learning, Databases etc) at the top of the Research Topic field on the application form. This does not commit you to work in this area, but helps us in ensuring the right people see your application.
To apply to the CDT in Data Science, use the standard University of Edinburgh website to apply for the programme called “Informatics: EPSRC Centre for Doctoral Training in Data Science: MSc by Research Data Science – 1 Year (Full-time)“. All CDT students should apply to this MSc programme as applications will automatically be considered for the full 1 year MSc by Research + 3 year PhD programme.
If you decide that you are more interested in other areas of computer science or informatics, the School of Informatics has many other postgraduate programmes available. Please see the School of Informatics pages about postgraduate study.
Please contact us if you have any queries.
Athena SWAN awards recognise and celebrate good practice on recruiting, retaining and promoting women in STEMM in academia. The School of Informatics achieved a Silver Award in 2017.