Wellcome Trust PhD Programme in Science

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.’ 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.

Our 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

Professor Rohini Mathur

Director; Professor and Chair of Health Data Science

Professor Jianhua Wu

Director; Professor of Biostatistics and Health Data Science

Professor Borislava Mihaylova

Professor of Health Economics; '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 Nina Fudge

Associate Director, Lecturer in Social Science

Dr Meredith Hawking

Associate Director, Lecturer in Social Science

Dr Harriet Larvin

Associate Director, Research Fellow in Health Data Science

Dr William Marsh

Associate Director, Senior Lecturer in Computer Science

Dr Moneeza Siddiqui

Associate Director; Lecturer in Genetic Epidemiology

Dr Runguo Wu

Associate Director, Senior Health Economist

Dr Eleanor Groves

Programme and Researcher Development Manager

 

For queries regarding the programme please contact hdip-dtp@qmul.ac.uk.

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.

We have a pool of supervisors across all three faculties. Students are support to assembles a supervisory team for their PhD project during the MRes and are required to co-produce a research proposal prior to starting their PhD.

Details of our current supervisor pool are below.

 

	Position	Research area	Department/School
Professor Ruth Ahnert	Professor of Literary History & Digital Humanities	Digital humanities; Quantitative network analysis; interdisciplinary theory, values and practice.	English and Drama
Professor Michael Barnes	Reader in Bioinformatics, Turing Fellow; Director, Centre for Translational Bioinformatics	Precision medicine, Multiomics/phenomics, AI/ML algorithm development for precision medicine	William Harvey Research Institute
Professor 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	School of Biological and Behavioural Sciences
Professor 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	Barts Cancer Institute
Dr Megan Clinch	Senior Lecturer in Medicine and Society	Social anthropology; complex interventions; qualitative methods; public engagement; socio-cultural interfaces	Wolfson Institute of Population Health
Professor Paul Curzon	Professor of Computer Science	Interaction Design; Human Computer Interaction	School of Electronic Engineering and Computer Science
Dr Anna Di Simoni	Clinical Lecturer in Primary Care	Primary care and health services translational research; digital interventions; medications adherence; self-management	Wolfson Institute of Population Health
Professor Norman Fenton	Professor of Risk Information Management, Turing Fellow	Risk assessment and decision-making under uncertainty using Bayesian networks	School of Electronic Engineering and Computer Science
Dr 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	Wolfson Institute of Population Health
Dr Nina Fudge	Social Scientist and Post-doctoral Research Associate	Social science, ethnography, anthropology, stroke, polypharmacy, translational research, citizen and patient participation	Wolfson Institute of Population Health
Dr Meredith Hawking	Lecturer in Social Science	Narrative research, qualitative methods, illness experience and practices, decision making, health communication, medicines adherence	Wolfson Institute of Population Health
Professor Patrick Healey	Professor of Human Interaction, Turing Fellow	Human communication using digital technologies; design for human interaction; digital capture	School of Electronic Engineering and Computer Science
Professor 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	Blizard Institute
Prof Richard Hooper	Professor in Medical Statistics	Efficient and innovative clinical trial design	Wolfson Institute of Population Health
Dr Silvia Liverani	Senior Lecturer in Statistics, Turing Fellow	Biostatistics, Statistics, Bayesian modelling, applications to epidemiology and spatial analyses	School of Mathematical Sciences
Professor Simon Lucas	Professor of Artificial Intelligence, Turing Fellow	Game AI, Machine Learning, Evolutionary Algorithms, Efficient Noisy Optimisation	School of Electronic Engineering and Computer Science
Dr 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	School of Electronic Engineering and Computer Science
Professor Isabelle Mareschal	Professor in Visual Cognition	Visual perception and non-verbal social communication; social neuroscience; judgements of gaze and facial expressions in clinical populations	School of Biological and Behavioural Sciences
Professor Rohini Mathur	Professor of Health Data Science	Application of causal approaches, observational epidemiology, and pharmacoepidemiology using large-scale routine electronic health data to inform best clinical practice, guidelines, and policy.	Wolfson Institute of Population Health
Professor Borislava Mihaylova	Professor of Health Economics	Health economics, decision modelling and evidence synthesis to inform health policy, economic evaluations, cost-effectiveness	Wolfson Institute of Population Health
Professor Ioannis Patras	Professor in Computer Vision and Human Sensing	Human behaviour analysis; Algorithmic design, User-centred algorithmic representations, Trustworthy Algorithms. Machine Learning	School of Electronic Engineering and Computer Science
Professor 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	William Harvey Research Institute
Professor Massimo Poesio	Professor in Computational Linguistics,Turing Fellow	Natural language processing; text mining	School of Electronic Engineering and Computer Science
Professor Stefan Priebe	Professor of Social and Community Psychiatry	Social psychiatry; social interactions; complex interventions; qualitative methods; patient-centred communication; clinical trials	Wolfson Institute of Population Health
Dr Matthew Purver	Reader in Computational Linguistics	Computational Linguistics	School of Electronic Engineering and Computer Science
Dr Clare Relton	Senior Lecturer in Clinical Trials	Innovative pragmatic trial designs using routinely collected health data, trials within cohorts’, registry trials	Wolfson Institute of Population Health
Professor Deborah Swinglehurst	Professor of Primary Care, NIHR Clinician Scientist	Qualitative methods, linguistic ethnography, discourse and narrative analysis; healthcare interaction and communication	Wolfson Institute of Population Health
Professor Stephanie Taylor	Professor in Public Health and Primary Care	Design & evaluation of complex, non- pharmacological interventions; supported self-management; long- term conditions	Wolfson Institute of Population Health
Dr Dayem Ullah	UKRI HDRUK; Rutherford Research Fellow	Health informatics, cancer epidemiology, pancreatic cancer, computational analysis and data mining for development for biological research.	Barts Cancer Institute
Professor Fiona Walter	Director of the Wolfson Institute of Population Health, and Professor of Primary Care Cancer Research	Randomised controlled trials, cohort studies and systematic reviews; patient experiences and qualitative and mixed methods approaches.	Wolfson Institute of Population Health
Professor Jianhua Wu	Professor of Biostatistics and Health Data Science	Modern statistical methods for applied health research with longitudinal data, EHR  and longitudinal cohorts to understand the disease trajectories, early detection and diagnosis, health inequalities, and risk factors for prevention; machine learning; data-mining; AI techniques.	Wolfson Institute of Population Health

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 students have access to our enrichment programme; a suite of researcher development workshops and activities designed to enhance their research training skills.

Find out more about researcher development on the programme.

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: 

• 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.

NHS

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

 

Industry

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

• MedCity
• Learning Health Solutions

 

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.

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.

Careers and Enterprise at Queen Mary

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, 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. Please not that medical, dental or nursing undergraduate degrees are not considered relevant disciplinary areas. For further information please see the application information page. 

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