Turing Institute PhD Studentships, UK

jeunes November 20, 2018 Scholarship in Europe Leave a comment 482 Views

Level: PhD
Course: PhDs linked to Data Science or Artificial Intelligence
Country: All
Value: A tax-free stipend of £20,500 per annum, a travel allowance and conference fund, and tuition fees for a period of 3.5 years.
No. of awards: 3
Deadline: 14th January 2019

More information

The Alan Turing Institute and Queen Mary University of London offer a number of places each year to motivated graduate students to complete a fully funded PhD. The Turing doctoral studentship scheme combines the strengths and expertise of world-class universities with the Turing’s unique position as the UK’s national institute for data science and artificial intelligence, to offer an exceptional PhD programme.

Turing doctoral students spend approximately half of their time based at the Institute headquarters at the British Library in London. They will apply and register for their doctorate at Queen Mary University of London, where they will spend the remainder of their time. Students will be supervised by faculty from Queen Mary who are also Fellows of the Institute or substantively engaged with it.

Why apply?

The Turing PhD programme offers all the benefits of completing a PhD at a world-class university, as well as a unique opportunity to study in a collaborative research space. Studying at The Alan Turing Institute offers students a unique opportunity to undertake a data science focused PhD in a multidisciplinary environment where more than 400 researchers from different disciplines work side-by-side. Studentships at the Turing support graduates to enrich their research through interdisciplinary engagement with the wider data science and artificial intelligence communities and the world at large.

To support students the Turing offers a generous tax-free stipend of £20,500 per annum, a travel allowance and conference fund, and tuition fees for a period of 3.5 years.

What are we looking for?

There is no standard Turing student. The Institute welcomes highly talented and proactive graduates who are significantly engaged in any of the topics core to data science and artificial intelligence and who are interested in engaging with the ethos of the Institute.

Participating in The Alan Turing Institute studentship scheme offers the opportunity to collaborate with academics and other students from a broad range of disciplines. The Institute is looking for students who will embrace the opportunity to enrich their research and broaden their learning through their time there. To this end, students must be willing to be based for at least half of their study at the Institute headquarters in London. Applications from a broad range of academic disciplines and backgrounds are encouraged, especially those whose research spans multiple disciplines and applications.

How to apply

Students should apply through Queen Mary. All applications should be made directly to the candidate’s chosen department and programme. In their application, and when choosing a supervisor, they should make it clear that they would like to be considered for the Turing doctoral studentship. PhD application pages can be selected from the drop-down menu here https://www.qmul.ac.uk/postgraduate/research/applying-for-a-phd/

Queen Mary will complete an initial assessment of your application and will refer selected candidates to the Institute in early February 2019. If successful, you will then be invited to attend a further interview at The Alan Turing Institute. The deadline for submission is midday Monday 14 January 2019.

The following is a list of supervisors listed by school or department at Queen Mary who are also Fellows of the Institute or substantively engaged with it.

School/Dept Barts Cancer Institute
Pedro Cutillas https://www.applieddatascience.qmul.ac.uk/people/p.cutillas
Trevor Graham https://www.applieddatascience.qmul.ac.uk/people/t.graham
Prabhakar Rajan https://www.applieddatascience.qmul.ac.uk/people/p.rajan

School/Dept Blizard Institute
Robert Lowe https://www.applieddatascience.qmul.ac.uk/people/r.lowe
John Robson https://www.applieddatascience.qmul.ac.uk/people/j.robson

School/Dept Business & Management
Panos Panagiotopoulos https://www.applieddatascience.qmul.ac.uk/people/p.panagioto…

School/Dept School of Biological and Chemical Sciences
Conrad Bessant https://www.applieddatascience.qmul.ac.uk/people/c.bessant
Magda Osman https://www.applieddatascience.qmul.ac.uk/people/m.osman
Elisabetta Versace https://www.applieddatascience.qmul.ac.uk/people/e.versace
Yannick Wurm https://www.applieddatascience.qmul.ac.uk/people/y.wurm

School/Dept School of Economics and Finance
Sebastian Axbard https://www.applieddatascience.qmul.ac.uk/people/s.axbard