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Below are answers to common questions; if you have any other questions please e-mail bigdata-cdt-enquiries@ncl.ac.uk .

What funding will I receive?

We have up to 11 studentships (for UK and EU domiciled students) for students commencing in September 2018 that will cover the costs of fees and a tax-free stipend (£14,553 each year) for 4 years. In addition there is significant funding for equipment, research costs and local, national and international travel (e.g. for conferences and placements). Funding for EU students who do not meet UK eligibility criteria is significantly more competitive. 

Can I join the EPSRC CDT in Cloud Computing for Big Data if I am not awarded a studentship?

Yes. We have up to 11 studentships for UK and EU domiciled applicants, but we have capacity to train up to 20 doctoral students each year. Applicants that are unsuccessful (or not eligible) in their application for one of our studentships, but that are still accepted for the EPSRC CDT in Cloud Computing for Big Data programme, will need to demonstrate that they can meet the cost of the fees currently £4450 per annum for UK and EU domiciled students; £15635 per annum for international students) and can support themselves financially for the full four years of the programme.

I’m interested in Cloud Computing for Big Data but I don’t have a degree in Computing, Mathematics or Statistics: can I apply?

If you have a degree in a numerate subject (for example Physics or Engineering) then please send an e-mail to bigdata-cdt-enquiries@ncl.ac.uk giving details of your academic background so we can assess whether you have a suitable background for the programme. Please include a CV, so we can fully assess your suitability. 

When can I apply?

We have rolling deadlines throughout the year, please check the apply page of our website or visit our Twitter feed for the most up to date deadlines. We advise applicants to apply early. 

You can informally contact us at any time, using the enquiries email address:  bigdata-cdt-enquiries@ncl.ac.uk

What if I miss the application deadline?

We will keep your details on file and contact you, if you wish to be considered for the following year's intake.

What are the International Language requirements? 

To study this course you need to meet our Band 8 English Language requirements:

Direct Entry: IELTS 7.0 overall (with a minimum of 6.5 in all sub-skills)

If you have lower English Language scores, you may be accepted onto a Pre-sessional English Language course. 

Our typical English Language requirements are listed as IELTS scores but we also accept a wide range of English Language tests.

The equivalent academic qualifications that we accept are listed on our country pages. 

How is the doctoral training structured?

Students spend the first 9 months taking taught modules, along with a group project before moving onto a PhD.  All students enrolling for the programme are initially registered for an MRes in Cloud Computing for Big Data. However, all students who satisfactorily complete the taught component will be transferred off the MRes programme and onto the PhD programme in June of Year 1, and are awarded a Postgraduate Diploma for completion of the taught component.
Any students who do not perform well on the taught component will remain on the MRes programme and begin work on an MRes dissertation. Funding for such students will terminate at the end of Year 1. Students continuing on the programme will concentrate mainly on their research projects during Years 2-4, but this will be augmented with addtional bespoke training and twice yearly research retreats for all CDT students.

What does the First Year involve?

The first year provides advanced Masters level training in cloud computing and data analytics before students move on to their PhD project.

Training will begin with a short "crash course" in either Computing science for mathematicians (for those with a Statistics background) or Statistics for computing scientists (for those from a Computer Science background). Students will then be taught as a unified cohort in topics including Statistics for Big DataProgramming for Big DataCloud ComputingMachine Learning,  Big Data Analytics and Time Series Analysis.  The taught component will finish with a substantial group project, with students from different backgrounds working together on a practical industry-focused data analysis problem.

Following this, and in years 2-4, students will carry out PhD research, guided by PhD supervisors from within the centre, and typically additional advisors from industry.

I have a maths and stats background. Will I cope with the computing and programming?

The taught programme is designed to be accessible to students who have done a mathematics and statistics degree containing a modest amount of computing and programming. It is expected that students entering the programme are generally comfortable with using computers and standard office software. You should also be familiar with at least one mathematical or statistical software package (such as R, or Matlab) and ideally have some basic programming experience, including writing simple functions. However, we do not expect you to have extensive programming experience, or expertise of general purpose programming languages such as Java. This is because training in programming will be provided during Year 1, and tutorial support will be available. R and Java will be the main programming languages used.

I have a computing background. Will I cope with the maths and stats?

The taught programme is designed to be accessible to students who have done a degree in computing science. It is expected that students entering the programme have mathematical knowledge at least equivalent to A-Level standard, but it is not expected that students will have a good knowledge of statistics on entering the programme. Intensive training in mathematics and statistics will be provided during Year 1, and tutorial support will be available.

Do I need a PhD project proposal when I apply?

No, you will develop your PhD proposal in the course of the first year. This allows time for you to identify which areas are matched to your interests and aptitudes.

Who will supervise my PhD?

Your supervision team will be determined as a result of discussions between yourself, the CDT co-directors, academics within the CDT and external collaborators.  During your first year you will get a chance to meet potential supervisors and hear about potential projects. You will normally have two academic supervisors – one with a statistical background and one from computer science. You will also normally have a third supervisor from the company or academic area that has the real-world problem your PhD will address.

Where will I be based?

You will be based in a new, purpose-designed space in a new building on Science Central, where you will be co-located with academics, researchers, and companies working in the area.

What is a placement?

During Year 2 or 3 all students will have the opportunity to take a fully funded placement for one to three months, hosted by an industrial or applied academic partner locally, nationally or internationally.

In Year 3, selected students will have the opportunity to take a placement at one of our University partners: The University of California at Berkley's AMPLab and  PUCRS Brazil.

Why might I prefer a CDT studentship to a conventional PhD position?

There are several reasons why students find CDT training more attractive than conventional PhD study. First, the studentships are fully funded for 4 years. Most PhDs take four years to complete, but conventional studentships provide only 3 (or occasionally 3.5) years funding. This means that many students pursuing a conventional PhD route suffer a degree of financial hardship while writing up their PhD thesis. Also, many students prefer the cohort approach to training adopted by CDTs. Most PhD students spend much of their first year learning about techniques that will be necessary for their research. For a conventional PhD project this is typically done by the student in isolation by reading monographs and journal articles. In a CDT, the cohort is trained together in a more focussed fashion, with  students having the opportunity to work together and share their experiences. The cohort approach to training also lends itself to our focussed activities such as our regular research "retreats", in which our students will go away as a cohort to present their work to one another and gain feedback from interested academic and industrial partners. Our CDT also has very strong links with industry, offering industry-linked projects and the opportunity for short term placements. This will help to develop relevant specialist and transferable skills, and improve the employability of our CDT graduates. CDTs also have dedicated administrative support, giving students a clear single point of contact for all University administration matters.

Finally, students will benefit from the social and inter-personal aspects of CDT training. As our CDT cohorts will be located together in a common space, students will come to know each other very well, and this will help maintain motivation over four years of study. Some students pursuing a conventional PhD route can find it a lonely experience, but within a CDT there are always many other students working on related projects with whom to discuss work and other issues.