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DeepMind PhD Studentship in AI or Machine Learning


  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 September 2022. 

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 Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £17,609 per year (2022-2023 rate to be announced) 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 and Machine Learning. 

Successful applicants will have the following profile:

  • Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
  • Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
  • Having obtained distinction or first class level degree is highly desirable
  • 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: eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to  by 12pm (noon), 11 April 2022:

  • 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: 11 April 2022

Applications will be reviewed by a panel of academic staff: May 2022

Interviews: May 2022

Start date: September 2022


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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