DeepMind PhD Studentship at Queen Mary, University of London

  School of Electronic Engineering and Computer Science

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 Funded PhD Programme (Students Worldwide)

About the Programme

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for 2020-2021

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind.

The PhD Studentship supports and encourages under-represented groups, namely female and BAME researchers, to pursue postgraduate research in AI, Machine Learning and Robotics.
The PhD DeepMind Studentship will cover tuition fees and offer a London stipend of £17,285 per year, together with an annual £2,000 travel and conference allowance and a one-off equipment grant of £1,500.

  • 3-year fully-funded PhD Studentship
  • Access to cutting-edge facilities and expertise in AI
  • Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply
Queen Mary is on the lookout for the best and brightest students in the fields of AI, Machine Learning and Robotics.
Successful applicants will have the following profile:

  • Identify as female and/or are from Black, Asian or a minority ethnicity, each being under-represented groups in the field of Artificial Intelligence;
  • Hold or be completing a Masters degree at distinction or first class level, or equivalent, in Computer Science, Electronic Engineering, AI, Physics or Mathematics;
  • Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths.

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants.

About the School of Electronic Engineering and Computer Science at Queen Mary
The DeepMind PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. The School is a dynamic community of approximately 150 PhD students and 80 research assistants working on research centred around a number of research groups in the areas of AI, computing and data science, networks, cognitive and creative computing.

The School's EPSRC Doctoral Training Centre in Media & Arts Technology (MAT) and UKRI Doctoral Training Centre in Artificial Intelligence and Music (AIM) and Intelligent Games and Game Intelligence (IGGI) has a number of exciting projects and several research groups are involved in the School's multidisciplinary research centres qMedia, the Centre for Intelligent Sensing and the Centre for Advanced Robotics.

How to apply
Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors:

Applicants should submit their interest by returning the following to by 12pm (noon), Friday 11 September:

  • Indicate first and second choice academic supervisor
  • CV (max 2 pages)
  • Cover letter (max 4,500 characters)
  • Research proposal (max 500 words)
  • 2 References
  • Certificate of English Language (for students whose first language is not English)
  • Other Certificates

Application deadline: 12pm (noon), Friday 11 September
Applications will be reviewed by a panel of academic staff: w/c 14 September
Interviews: w/c 21 September
Start date: October 2020 or January 2021

Funding Notes

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

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