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Institute of Population Health Sciences (IPHS)

Wellcome Trust PhD Programme in Science

Health data in practice: human-centred science

Research area: Population and Public Health

Applications for the 2021 round are now closed. Applications for entry in September 2022 will open later this year.

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A group of students looking over some data
A group of students looking over some data

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.

A busy street with crowds at Whitechapel market looking towards The City of LondonThe 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.’ 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.

An aerial image looking over east London with The Royal London Hospital in the centre and The City of London in the backgroundOur east London location

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.


Programme management team

Carol DezateuxProfessor Carol Dezateux

Programme Director; Professor of Clinical Epidemiology and Health Data Science; Associate Director, Health Data Research UK; ‘Actionable Information’ Theme Leader




Sandra EldridgeProfessor Sandra Eldridge

Professor of Biostatistics, Director of Pragmatic Clinical Trials Unit; ‘Effective and Efficient Evaluation’ Theme Leader




Patrick HealeyProfessor Patrick Healey

Professor of Human Interaction; ‘Human-Data Interaction’ Theme Leader




Deborah SwinglehurstProfessor Deborah Swinglehurst

Professor of Primary Care, Director of Postgraduate Studies; ‘Health Data in Practice’ Theme Leader




Eleanor GrovesDr Eleanor Groves

Programme Manager




For queries regarding the programme please contact

Year 1: MRes in Health Data in Practice

The MRes programme will provide:

  • an introduction to key concepts and research methods and analysis highlighting relevant methodological issues and challenges that comprise the interdisciplinary foundations of the programme and will include an introduction to research design, qualitative and quantitative methods, and societal and ethical issues related to data technologies
  • experience of possible research areas for further study including opportunities to apply the knowledge gained and undertake a small research project during the MRes year
  • an environment in which you can develop a solid understanding of the wider context of health data and critical thinking in this field. 

The programme comprises:

  • Three compulsory taught modules (worth 75 credits in total)
  • Three optional taught modules (worth 15 credits each)
  • One project module (worth 60 credits)

Total – 180 credits: Level 7

Full breakdown of modules

Years 2-4: PhD

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.

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:

  • Human-data interaction
  • Health data in practice
  • Effective and efficient evaluation
  • Actionable information

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.

Potential supervisors

NamePositionResearch area

Ruth Ahnert

Senior Lecturer in Renaissance Studies, Turing Fellow

Digital humanities; Quantitative network analysis; interdisciplinary theory, values and practice.

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

UKRI HDRUK Rutherford Research Fellow

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

UKRI HDRUK Rutherford Research Fellow


Public health; linkage of geography and environment linked to electronic health records; data science; wider determinants of health outcomes

Meredith Hawking 

Post-doctoral research fellow in Social Science

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 

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

Rutherford Research Fellow

Health informatics, cancer epidemiology, pancreatic cancer, computational analysis and data mining for development for biological research.

Students taking part in a writing workshopOur 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.


Students working in a groupOur 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:

RDF framework, 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: 

  • supports creativity, with ambitious and collaborative working across disciplines and institutions 
  • prioritises diversity and inclusion, so that everyone benefits from supportive relationships no matter what their background
  • produces open research, which is conducted with honesty and integrity.


Research environment

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:

  • Barts Health NHS Trust
  • Primary Care Networks and general practices linked to the Clinical Effectiveness Group
  • Public Health departments and clinical commissioning groups in Tower Hamlets, City & Hackney, and Newham
  • The Discovery Programme: data publishers and stakeholders



The following organisations will provide access to industry placements for students and potential research collaborations:


Other UK research organisations

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.

A careers adviserYou 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.

Careers and Enterprise at Queen Mary

A student meeting with a careers adviserQueen 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, is available on 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. Relevant subjects include quantitative disciplines such as Statistics, Computer Sciences, Mathematics, Bioinformatics and Biomedical Sciences, and qualitative disciplines such as Anthropology, Ethnography and Social Sciences.

Candidates with other relevant qualifications or research experience may also be eligible.

Successful candidates will display their passion for a career in research, good communication skills and scientific knowledge of the field.