Research area: Population and Public Health
Applications for this round are now closed. Application dates for 2021 entry will be advertised soon.
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Our Wellcome-funded doctoral training programme applies human-centred data research to health and care data, and will introduce you to a wider context for your research, enabling you to draw on concepts, disciplines and methods underpinning algorithmic designs, sensing and data capture, human-interactions, qualitative and quantitative evaluation and decision-making, in real-world settings. You will develop as a future scientific leader able to apply interdisciplinary perspectives to your research and realise the potential of innovations in health data research for the benefit of patients, the public, health care systems, and society.
The Wellcome Trust Health Data in Practice programme combines scientific excellence with a commitment to improving the working environment and transition support for trainees. We commit to being part of an evolving community of practitioners who will develop and share practice to bring science and culture together, placing both firmly at the heart of what we do.
The availability, scale and depth of data collected in the course of health care, by or about patients – combined with data-driven approaches to its analysis – is creating a paradigm shift in health care and its delivery. Machine learning and other automated methods of analysis will only succeed in providing useful insights for health and care if we understand health data in practice: how data is actually generated, interpreted and used.
Human-centred data science operates ‘at the intersection of human-computer interaction, computer-supported cooperative work, human computation, and statistical and computational techniques of data science’ while preserving ‘the richness associated with traditional methods while utilising the power of large data sets’ and encouraging critical social perspectives on health data in practice. We adapt this concept to the 'health data in practice' doctoral training programme with the goal of developing highly skilled future leaders able to apply interdisciplinary perspectives to research and innovations in health data science for the benefit of patients, the public, health care systems and society.
Barts and The London is committed to pioneering medical education and research. Being firmly embedded within our east London community, and with an approach to education and research that is driven by the specific health needs of our diverse population, the programme will draw on these networks to enhance your research.
Professor Carol Dezateux
Programme Director; Professor of Clinical Epidemiology and Health Data Science; Associate Director, Health Data Research UK; ‘Actionable Information’ Theme Leader
Professor Sandra Eldridge
Professor of Biostatistics, Director of Pragmatic Clinical Trials Unit; ‘Effective and Efficient Evaluation’ Theme Leader
Professor Patrick Healey
Professor of Human Interaction; ‘Human-Data Interaction’ Theme Leader
Professor Deborah Swinglehurst
Professor of Primary Care, Director of Postgraduate Studies; ‘Health Data in Practice’ Theme Leader
Dr Eleanor Groves
[Interim] Programme Manager
For queries regarding the programme please contact hdip-dtp@qmul.ac.uk.
The MRes programme will provide:
The programme comprises:
Total – 180 credits: Level 7
See full breakdown of modules
The programme is framed in four scientific themes: Human-data interaction; Health data in practice; Effective and efficient evaluation; and Actionable information. You will work with your supervisors to develop an interdisciplinary research proposal within one of these four themes.
See more information on the themes
You are expected to pass the six- to nine-month progression via a 2,000-word report and a short 10-minute oral presentation to a Progression Panel comprising supervisor(s), relevant Director of Graduate Studies and the Programme Director or Co-Directors.
There are further milestones at 18 months (20,000 word report to the same assessors), and again at 30 months (to include additional data since the 18 month report plus a plan of the thesis).
Transfer to write-up status in the final year is considered a progression point and submission of the thesis within four years is a requirement.
You will also be supported to submit at least one manuscript and to present at relevant national/international scientific meetings during your studentship.
Each student will have a clear supervision plan with regular supervisions. The Director will meet with you on an annual basis to reflect on your year and get feedback on your progression, and will operate an open door policy at other times of the year if additional support is needed.
You will be supervised by at least two research active supervisors from different disciplines who will work with you to develop an interdisciplinary research proposal within one of four themes:
Learn more about the themes
If appropriate, the supervisory team will include a member from a partner organisation (NHS, industry).
The programme brings together internationally-leading scientists with strong track records in research and PhD supervisions spanning biological, clinical, population, computer, and social sciences, as well as the arts and humanities, including digital humanities and bioethics. It harnesses the breadth and depth of expertise at our internationally leading research-intensive university, and our membership of the Health Data Research (HDR) UK and Alan Turing Institutes, building on our strong local partnerships with health professionals, patients and the public in seldom-heard disadvantaged populations receiving health care in real world settings.
Ruth Ahnert
Senior Lecturer in Renaissance Studies, Turing Fellow
Digital humanities; Quantitative network analysis; interdisciplinary theory, values and practice.
Richard Ashcroft
Professor of Bioethics, Turing Fellow, Co-director Centre for the Study of Incentives in Health
Bioethics, research ethics, public health ethics;
Michael Barnes
Reader in Bioinformatics, Turing Fellow; Director, Centre for Translational Bioinformatics
Precision medicine, Multiomics/phenomics, AI/ML algorithm development for precision medicine
Conrad Bessant
Professor of Bioinformatics, Turing Fellows, Co-Director, Computational Biology Centre
Multi-omics and integration of health data; statistics, machine learning, sequence analysis, proteome informatics, software development
Adriano Barbosa da Silva
UKRI HDRUK Rutherford Research Fellow
Integrated analysis of electronic health records, genetic, genomic and cardiac magnetic resonance imaging; translational data
Kamaldeep Bhui
Professor of Cultural Psychiatry and Epidemiology
Health inequalities, social exclusion, work characteristics, cultural psychiatry, health services and public health
Claude Chelala
Professor of Bioinformatics, Training Lead, HDRUK London; Co-Director, Computational Biology Centre
Precision medicine; data mining; computational and integrative bioinformatics; integrated genomic and electronic health records; cancer outcomes
Megan Clinch
Senior Lecturer in Medicine and Society
Social anthropology; complex interventions; qualitative methods; public engagement; socio-cultural interfaces
Kit Curtius
Mathematical modelling;data science; intersection of applied mathematics, biology, and clinical translation; cancer evolution and screening
Paul Curzon
Professor of Computer Science
Interaction Design; Human Computer Interaction
Carol Dezateux
Professor of Clinical Epidemiology and Health Data Science, Associate Director HDRUK London
Life course epidemiology; actionable integrated health records; comparative effectiveness; learning health systems; public partnerships
Anna Di Simoni
Clinical Lecturer in Primary Care
Primary care and health services translational research; digital interventions; medications adherence; self-management
Anna Dowrick
Post-doctoral research fellow in Social Science
Complex interventions; guidelines and technologies in practice; qualitative methods; evaluation; inequalities; gender;critical policy analysis
Sandra Eldridge
Professor of Biostatistics; Director, Pragmatic Clinical Trials Unit
Cluster randomised trials; complex interventions particularly in primary care; stepped wedge designs; methodological research
Norman Fenton
Professor of Risk Information Management, Turing Fellow
Risk assessment and decision-making under uncertainty using Bayesian networks
Sarah Finer
Clinical Senior Lecturer in Diabetes; Honorary Consultant in Diabetes
Translational genomics, data science and health services research, type 2 diabetes, prevention and treatment in ethnically diverse communities
Nina Fudge
Social Scientist and Post-doctoral Research Associate
Social science, ethnography, anthropology, stroke, polypharmacy, translational research, citizen and patient participation
Gillian Harper
Public health; linkage of geography and environment linked to electronic health records; data science; wider determinants of health outcomes
Meredith Hawking
Narrative research, qualitative methods, illness experience and practices, decision making, health communication, medicines adherence
Patrick Healey
Professor of Human Interaction, Turing Fellow
Human communication using digital technologies; design for human interaction; digital capture
David van Heel
Professor of Genetics; Director, East London Genes & Health Study
Population genetic cohorts in ethnically diverse communities, ‘human knockouts’, human autoimmune diseases, phenotyping, precision medicine
Richard Hooper
Reader in Medical Statistics
Efficient and innovative clinical trial design
Silvia Liverani
Senior Lecturer in Statistics, Turing Fellow
Biostatistics, Statistics, Bayesian modelling, applications to epidemiology and spatial analyses
Simon Lucas
Professor of Artificial Intelligence, Turing Fellow
Game AI, Machine Learning, Evolutionary Algorithms, Efficient Noisy Optimisation
William Marsh
Senior Lecturer in Computer Science, Turing Fellow
Data analytics, machine learning and probabilistic modelling for decision support in medical applications; Safety, reliability and risk
Isabelle Mareschal
Visual perception and non-verbal social communication; social neuroscience; judgements of gaze and facial expressions in clinical populations
Borislava Mihaylova
Professor of Health Economics
Health economics, decision modelling and evidence synthesis to inform health policy, economic evaluations, cost-effectiveness
Magda Osman
Reader in Experimental Psychology, Turing Fellow
Judgment and Decision-making under risk and uncertainty
Ioannis Patras
Professor in Computer Vision and Human Sensing
Human behaviour analysis; Algorithmic design, User-centred algorithmic representations, Trustworthy Algorithms. Machine Learning
Steffen Petersen
Professor of Cardiovascular Medicine, Turing Fellow, Centre Lead, Advanced Cardiovascular Imaging
AI in healthcare/imaging, Health data science, cardiovascular magnetic resonance, UK Biobank, health economics
Massimo Poesio
Professor in Computational Linguistics,Turing Fellow
Natural language processing; text mining
Stefan Priebe
Professor of Social and Community Psychiatry
Social psychiatry; social interactions; complex interventions; qualitative methods; patient-centred communication; clinical trials
Matthew Purver
Reader in Computational Linguistics
Computational Linguistics
Clare Relton
Senior Lecturer in Clinical Trials
Innovative pragmatic trial designs using routinely collected health data, trials within cohorts’, registry trials
Deborah Swinglehurst
Professor of Primary Care, NIHR Clinician Scientist
Qualitative methods, linguistic ethnography, discourse and narrative analysis; healthcare interaction and communication
Stephanie Taylor
Professor in Public Health and Primary Care
Design & evaluation of complex, non- pharmacological interventions; supported self-management; long- term conditions
Dayem Ullah
UKRI HDRUKRutherford Research Fellow
Health informatics, cancer epidemiology, pancreatic cancer, computational analysis and data mining for development for biological research.
Our training programme will introduce you to a wide context for your science and enable your development as ‘human-centred’ health data scientists capable of drawing on diverse concepts and disciplines and of engaging with real-world settings. Our programme leverages the strong track record of Queen Mary in developing and sustaining an innovative, inclusive and empowering research culture for its students and staff.
Our Researcher Development unit provides a comprehensive spectrum of courses and a range of support for PhD students including language training, presentation, critical appraisal, reading and writing skills, leadership, inner coaching, and resilience, time and project management, public engagement and communication skills. You will be expected to complete at least 210 hours of training and development during the course of the PhD, with a minimum required in each of the following four UKRI domains*: Knowledge and intellectual abilities, Personal Effectiveness, Research Governance and Organisation, and Engagement, Influence and Impact. You will also be offered transferable skills training which is reviewed at supervisions when objectives are set.
Every PhD student has a personal tutor who is independent of their academic supervisors, and who monitors their academic progress and provides pastoral care when needed.
*UKRI domains:
Our vision is to create a vibrant and inclusive interdisciplinary learning environment, which signals the importance we attach to diversity and inclusivity in our interactions, and which provide a safe and welcoming environment in which individuals can flourish and create. Our vision is aligned with that of the Wellcome Trust's Reimagining Research group, which aims to create a research culture that:
You will access science in internationally-leading centres, at the forefront of the science and execution of pragmatic clinical trials, and innovative evaluation of complex interventions and social practice in health care, incorporating ethnographic approaches. It includes leading computer scientists researching human interactions and decision-making, and the development and communication of trustworthy and explainable algorithms, as well as research groups from psychology, digital humanities, bioethics, linguistics and drama applied to health, with whom they interact. It will be closely integrated with the work of the Institute for Advanced Data Science, and Centres for Intelligent Sensing and Advanced Robotics at Queen Mary. Our leadership in the Discovery Programme – an innovative near real-time integrated health record for 2.2 million people – presents an unprecedented and trusted opportunity to access this important and unique asset to understand health data in practice.
The following NHS organisations will provide access to internships and placements, access to clinicians, patients and commissioners, and channels for dissemination and public engagement:
The following organisations will provide access to industry placements for students and potential research collaborations:
HDRUK and The Alan Turing Institute: opportunities for internships, placements and exchange visits including leveraging this site’s international links in Canada and Australia and their role as Welsh Administrative Data Research UK hub.
You will be supported in your career transition through individualised, coherent career management support with access to mobility opportunities, mentoring, enterprise support and internships, as well as embedding integrated placements and internships within the training pathways.
At the end of the third year, you will meet with the Programme Director and your supervisor to discuss career transitions and develop a plan for placements, training and skills development. You will also have the opportunity to apply to the Programme Transition Fund.
The School provides support for work and industry placements for doctoral students reaching the end of their PhD and transitioning into new roles. As well as working closely with the NHS, we have access to industry partners through MedCity’s Collaborate to Innovate programme. Students can access an eight-week QIncubator programme and QConsult – QMUL’s award-winning employability programme.
Queen Mary has two careers consultants dedicated to supporting PhD students, which together with a wide range of events, including PhD alumni discussion panels, speed networking events, and employer-led discussions, can help you to go on to a wide range of careers within academia and beyond.
While conducting your projects, you will receive advice, training and support around project management and presentation skills from the Queen Mary Careers and Enterprise team which also offers a range of support to prepare students for job applications, interviews, work experience, and career choices.
Queen Mary also provides support for PhD students and women researchers from all backgrounds, ages and stages of their lives through the Springboard Women’s Development Programme.
Eligibility information and details of resources the studentships provide (including stipend) can be found of the Wellcome website.
You must be a graduate or student who has, or expects to obtain, at least an upper second-class degree (or equivalent for EU and overseas candidates) in a relevant subject, which includes (but not confined to) statistics; computer sciences; mathematics; bioinformatics; psychology; biomedical sciences as well as qualitative disciplines including (but not confined to) anthropology; sociology; science and technology studies; and organisation studies.
Candidates with other relevant qualifications or research experience may also be eligible. Please contact us if you would like to discuss your eligibility.
Successful candidates will display their passion for a career in research, good communication skills and scientific knowledge of the field.