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.
Course: PhDs linked to Data Science or Artificial Intelligence
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
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.
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
School/Dept School of Electronic Engineering and Computer Science
Gianni Antichi https://www.applieddatascience.qmul.ac.uk/people/g.antichi
Emmanouil Benetos https://www.applieddatascience.qmul.ac.uk/people/emmanouil.b…
Andrea Cavallaro https://www.applieddatascience.qmul.ac.uk/people/a.cavallaro
Anthony Constantinou https://www.applieddatascience.qmul.ac.uk/people/a.constanti…
Felix Cuadrado https://www.applieddatascience.qmul.ac.uk/people/felix.cuadr…
Ildar Farkhatdinov https://www.applieddatascience.qmul.ac.uk/people/i.farkhatdi…
Norman Fenton https://www.applieddatascience.qmul.ac.uk/people/n.fenton
Sean Gong https://www.applieddatascience.qmul.ac.uk/people/s.gong
Pat Healey https://www.applieddatascience.qmul.ac.uk/people/p.healey
Julian Hough https://www.applieddatascience.qmul.ac.uk/people/j.hough
Lorenzo Jamone https://www.applieddatascience.qmul.ac.uk/people/l.jamone
Simon Lucas https://www.applieddatascience.qmul.ac.uk/people/simon.lucas
William Marsh https://www.applieddatascience.qmul.ac.uk/people/d.w.r.marsh
Martin Neil https://www.applieddatascience.qmul.ac.uk/people/m.neil
Massimo Poesio https://www.applieddatascience.qmul.ac.uk/people/m.poesio
Mark Sandler https://www.applieddatascience.qmul.ac.uk/people/mark.sandle…
Fabrizio Smeraldi https://www.applieddatascience.qmul.ac.uk/people/f.smeraldi
Dan Stowell https://www.applieddatascience.qmul.ac.uk/people/dan.stowell
Gareth Tyson https://www.applieddatascience.qmul.ac.uk/people/g.tyson
Steve Uhlig https://www.applieddatascience.qmul.ac.uk/people/steve.uhlig
School/Dept School of Engineering and Materials Science
Kaspar Althoefer https://www.applieddatascience.qmul.ac.uk/people/k.althoefer
Jun Chen https://www.applieddatascience.qmul.ac.uk/people/jun.chen
School/Dept School of Law
Richard Ashcroft https://www.applieddatascience.qmul.ac.uk/people/r.ashcroft
School/Dept School of Mathematical Sciences
Ginestra Bianconi https://www.applieddatascience.qmul.ac.uk/people/g.bianconi
Michael Farber https://www.applieddatascience.qmul.ac.uk/people/m.farber
Kathrin Glau https://www.applieddatascience.qmul.ac.uk/people/k.glau
Vito Latora https://www.applieddatascience.qmul.ac.uk/people/v.latora
Silvia Liverani https://www.applieddatascience.qmul.ac.uk/people/s.liverani
John Moriarty https://www.applieddatascience.qmul.ac.uk/people/j.moriarty
Primoz Skraba https://www.qmul.ac.uk/maths/profiles/skrabaprimoz.html
School/Dept School of Physics and Astronomy
Adrian Bevan https://www.applieddatascience.qmul.ac.uk/people/a.j.bevan
Eram Rizvi https://www.applieddatascience.qmul.ac.uk/people/e.rizvi
School/Dept William Harvey Research Institute
Michael R. Barnes https://www.applieddatascience.qmul.ac.uk/people/m.r.barnes
Steffen Petersen https://www.applieddatascience.qmul.ac.uk/people/s.e.peterse…
School/Dept Wolfson Institute for Preventive Medicine
Adam Brentnall https://www.applieddatascience.qmul.ac.uk/people/a.brentnall
Mark Freestone https://www.applieddatascience.qmul.ac.uk/people/m.c.freesto…
Find out more by contacting an above-named supervisor or here http://turing.ac.uk/PhD