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Data-Centric Engineering
Data-Centric Enginering programme. Credit:Queen Mary University of London
  1. Data-Centric Engineering
  2. Training

Training Programme Structure

The CDT in Data-Centric Engineering offers a novel, enriched and integrated experience in applied research by adopting a high-level, student-centred, cohort approach to research and professional training.

Through the programme we wish to build a world-class cohort of high-tech entrepreneurs and technology leaders who have followed non-typical routes to doctoral training. 

Our Doctoral Scholars will enrol on a 4 year full-time programme and qualify with an Engineering Doctorate (EngD) in Data-Centric Engineering.

As a Doctoral Scholar you will have the opportunity to apply research in industry settings and receive supervision from an experienced academic from Queen Mary.

Additional training benefits include:

  • access to all research facilties including high performance computing as appropriate
  • funding to attend international research conferences
  • financial assistance for child-care or carers during residential activities away from home 

Our Programme in a Nutshell 

The training comprises a four-year programme, with an integrated programme of taught courses, workplace based research projects and professional development training running throughout. 

 The programme has four main pillars:

  1. Advanced taught courses: a bespoke programme of 6 taught courses in years 1-3*
  2. Researcher development training of the cohort throughout years 1-4
  3. Industry placement throughout years 1-4 (EDS route only)
  4. Research projects throughout years 1-4: our Scholars undertake, under the expert supervision of a team of QMUL academic staff, either one doctoral-level research project, or up to 4 smaller projects, which taken together make a substantial contribution to knowledge at doctoral level. 

* In years 1-2, our Scholars attend 3 core research courses:

  • An Introduction to Research Methods and Responsible Innovation. All our Scholars have been out of academic education for at least 3 years - some of them for a decade or two. Our first core course has been specifically designed to help our Scholars (re-)integrate into university study, and get up to speed with the requirements of a Doctorate. See the SAMPLE timetable for semester 1 - RMRI course course [PDF 85KB] 
  • Statistical Thinking and Engineering Mathematics
  • Machine Learning

In years 2-3 Scholars take 3 Elective Modules. Electives are selected on the basis of your skills and knowledge development needs in relation to your research project(s). Electives include a wide range of relevant data intensive science and engineering courses available at Queen Mary across the Faculty of Science and Engineering (subject to the approval of the supervisor and Director of Training). Below are just some examples and Scholars will be able to access a more comprehensive list once enrolled. 

Time Commitment:

Enterprise Doctoral Scholars (EDSs) are full-time students and are expected to dedicate 35 hours per week on average to their studies, research, and training. EDSs will also undertake industrial placement(s) during the programme. 

Professional Doctoral Scholars (PDSs) are employed within a partner organisation, usually in a Research & Development role. They keep their role within their company, and their also register as full-time students. They carry our the bulk of their research at work, under the supervision of an industrial supervisor and two QMUL academic experts. We help them negotiate time release to participate in the taught and training elements of our programme (one day a week on average, mostly during term time). For more information, please contact dce-cdt@qmul.ac.uk. 

Our Centre Director Eram and our Centre Manager Gabriella tell us more about how our EngD training programme works in the videos below.

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