Internet Explorer is no longer supported by Microsoft. To browse the NIHR site please use a modern, secure browser like Google Chrome, Mozilla Firefox, or Microsoft Edge.

NIHR Pre-doctoral Fellowship Potential Supervisors


Published: 24 January 2023

Version: 2.0 (Jan 24)

Print this document

When applying for the Pre-Doctoral Fellowship, it is expected that applicants will have the help and support from two or more supervisors when putting together the application, in particular, details of the training and development programme. Supervisors will also play an active role in supporting the applicant throughout the duration of the fellowship itself. At least one of the intended supervisors must be within the selected research methodology proposed for study.

For those applicants that have yet to identify a supervisor, the list below contains the names and contact details of individuals willing to provide support in putting together an application, and with undertaking the fellowship. This list is not exhaustive and applicants may approach other potential supervisors as required.

For further information, please refer to the latest set of guidance notes listed on the NIHR website.

University of Birmingham

Hareth Al-Janabi


Support areas: Health economics, especially supporting applications relating to:

  • Priority setting
  • Power in the health and care economy
  • Evidence-policy relationship
  • Health and care funding
  • Qualitative and mixed research methods in health economics
  • Economics of mental health
  • Informal care and societal perspective

Karla Hemming


Support areas: I am a statistician with an interest in cluster randomised trials. My research interests range from statistical (including sample size and analysis issues) to the more practical (including reporting, bias and ethical issues).

Emma Frew


Support areas: I am a NIHR Research Professor in health economics with a particular interest in using economics to inform policy to tackle population obesity. I work closely with local authorities, commercial stakeholders and third sector organisations. I am especially interested in supporting applications relating to methodological challenges of using health economics in non-health contexts. 

Professor Sue Jowett


Support areas: Specialises in applied trial and model-based economic evaluation. Broad clinical area of interest is chronic disease with emphasis on chronic respiratory disease, musculoskeletal disease and cardiovascular disease. Other linked areas of interest are multimorbidity and health economic aspects of air pollution.

Dr Louise Jackson


Support areas: Louise Jackson’s research interests relate to methods of economic evaluation, and she is particularly interested in methodological issues relating to the evaluation of public health and digital health interventions. Louise’s applied areas of interest include sexual health, obstetrics and gynaecology, women’s health and global health.

University of Bristol

Professor Penny Whiting


Support areas: I am Professor of Clinical Epidemiology based in Population Health Sciences, Bristol Medical School. My research interests focus on diagnostic test evaluation, systematic reviews and developing tools to assess risk of bias in epidemiological studies. I lead the MSc Epidemiology at University of Bristol and am Co-Director of Bristol Technology Appraisal Group. I am able to support projects in Systematic Reviews; Health Technology Assessment; Diagnostic Test Evaluation; Risk of Bias Tools. Please contact directly for further information.

University of East Anglia

Tracy Sach


Support areas: I am a Professor in Health Economics with an interest in the methods and application of economic evaluation within clinical trials. I also undertake work in the outcome measurement area. I have applied these methods in a number of clinical areas but have a particular focus on children and young people, dermatology, older people and rehabilitation.

Keele University

Professor Christian Mallen


Support areas: Dean of Keele Medical School and NIHR Research Professor in General Practice. 

University of Leicester

Sylwia Bujkiewicz


Support areas: I am a Professor in Biostatistics with interest in Bayesian methods for evidence synthesis to support policy decisions in health care. I am interested in developing methods that allow for combining data from different types of studies (e.g. clinical trials and observational studies), different types of outcomes, treatments, or from multiple disease types, to effectively evaluate new treatments. I am also interested in applying the methods to different clinical areas and investigating what impact the methods may have on the results of health economic models which support HTA policy recommendations; for example, by NICE.

Nicola Cooper


Support areas: I am a health economist and statistician with an interest in health technology appraisals i.e. how well new interventions work and whether they represent good value for money. I am particularly interested in supporting applications relating to the methodological challenges of combining evidence from multiple trials to inform economic evaluations as well as how best to effectively communicate data and results from these complex analyses to a range of end-users through the development of novel interactive visual displays.

Suzanne Freeman


Support areas: I am a Lecturer in Medical Statistics. My research focuses on developing and applying methods for combining data from multiple clinical trials (known as meta-analysis). I also have an interest in developing and applying methods for meta-analysis with continuous or time-to-event outcomes.

Laura Gray


Support areas: I am a Professor in Medical Statistics. I’m interested in how we can best design and analyse clinical trials, particular those testing non-drug interventions in people with complex care needs, which can be more challenging than standard drug trials. I am also interested in how we can involve members of the public as partners in methodological research.

Mark Rutherford


Support areas: I am an Associate Professor of Biostatistics based at the University of Leicester. My main areas of research interest are in methods for the analysis of population-based cancer data. This generally involves looking at factors that explain variation in how long people survive following a diagnosis of cancer. I’m also interested in methods to further understand the reasons for differences in cancer survival we see across population groups. I have an interest in survival analysis methods in general – including extensions to competing risks (i.e. looking at the impact of other possible outcomes on a main event of interest), multistate models, and methods for extrapolating survival curves to assess the long-term benefits of new treatments. I’d also be interested in developing projects around making realistic synthetic data – to enable data to be released to other researchers without risking privacy concerns.

Sarah Seaton


Support areas: I am able to support mixed methods research related to perinatal and paediatric health. I have research experience in using complex statistical methods to analyse routine data sources, data linkage and interviewing parents. My specific interests are in the intensive care needs of children aged <2 years.

Lucy Smith


Support areas: I am able to support advanced quantitative, epidemiological or mixed methods research particularly relating to improving maternal and newborn health such as statistical methodology around the measurement and monitoring of adverse pregnancy outcomes, qualitative research to understand parents’ and clinicians’ experiences of pregnancy loss, neonatal mortality and preterm birth and the use of large-scale routine health data to monitor and reduce inequalities in health.

Alex Sutton


Support areas: I am a Professor of Medical Statistics with a special interest in how to synthesise information from multiple studies. As well as developing new statistical methodology, I am also interested in the development of web-based apps for implementation of analysis and interactive data visualisation methods. I also do research on how to improve the design of future studies through considering how their results will combine with those from existing studies.

Lucy Teece


Support areas: I am a Lecturer in Medical Statistics. I work with a range of clinicians using real data to analyse problems related to gynaecological cancers, autism and learning disabilities, and orthopaedic surgery (joints). As well as working on many applied projects, my research interests include prognosis research and health inequalities.

Imperial College London

Dr Leila Janani


Support areas: I am a Research Fellow /Senior Statistician and deputy head of trial methodology at the Imperial clinical trials unit. I am interested in supervising fellows who are interested in working on:

  • Designing flexible and adaptive clinical trials
  • Using the Bayesian approach in the analysis of clinical trials
  • Improving the design and analysis of critical care trials

Professor Céire Costelloe

Email: or

Support areas: Routine dataset, causal inference, natural experiments and quasi experimental design, interest in infection in particular.

Rachel Phillips


Support areas: Senior lecturer in medical statistics working on applied clinical trials and trials methodology. I am interested in supervising individuals interested in undertaking a project related to the area of adverse events in clinical trials.

Dr Suzie Cro


Support areas: I am a senior lecturer in medical statistics and clinical trials and head of trial methodology, co-head of statistics at Imperial Clinical Trials Unit. I would be pleased to supervise fellows who are interested in:

  • Estimands and the handling of post-randomisation intercurrent events, such as rescue medication and treatment non-compliance in randomised controlled trials
  • Adaptive trial designs and the implementation of these
  • The handling of missing data in randomised controlled trials

London School of Hygiene and Tropical Medicine

Jonathan Bartlett


Support areas: I am Professor of Medical Statistics in the Medical Statistics Department at LSHTM. I would be pleased to supervise fellows working on statistical methods for:

  • missing data
  • causal inference
  • clinical trials

Pierre Masselot


Support areas: environmental epidemiology, environmental health, climate change, machine learning, time series

Matthew J. Smith


Support areas: causal inference, target trials, machine learning, missing data, simulation studies, cancer survival

Linda Sharples


Support areas: medical statistics, clinical trials

King's College London

Dr Alfredo Iacoangeli


Support areas: My research focusses on the use and development of state-of-the-art bioinformatics, statistical and Machine Learning methodologies and large multi-omics datasets to investigate the relationship between genotypes, environment and biology of with a focus on human diseases.

I lead a team of computational scientists. Our main projects can be split into two classes: i) the development and use of bioinformatics, machine learning and statistical methods to analyse large multi-omics and clinical datasets, to study the biological basis of human diseases; ii) the development of bioinformatics solutions for precision medicine and to allow scientists without extensive informatics expertise to take advantage of publicly available large biodata.

Professor Lorna Fraser


Support areas: Professor of Palliative Care and Child Health - My areas of expertise lie in the secondary data analyses of routinely collected health and administrative datasets especially, but not exclusively, in areas of child health research.

Sabine Landau and Ulrike Schmidt

Email: and 

Support areas: Sabine Landau is Professor of Biostatistics at the Institute of Psychiatry, Psychology and Neuroscience, King’s College London and leads a research programme on causal modelling and evaluation. Ulrike Schmidt is a Consultant Psychiatrist, Professor of Eating Disorders and NIHR Senior Investigator. We have collaborated on numerous trial evaluations of interventions to improve outcomes for patients with eating disorders, providing a unique UK data resource that can be explored to gain insights beyond treatment effectiveness (including data from the NIHR ARIADNE, TRIANGLE and DAISIES trials).

We offer a methodology training programme in causal modelling and efficacy and mechanisms evaluation. The fellow will be formally trained as a statistician/data scientist in applied health research by attending the MSc in Applied Statistical Modelling and Health Informatics. In addition, by working on a project as part of our collaborative team of clinicians, eating disorders researchers and statisticians, the fellow will have the opportunity to gain practical experience.

The research team already knows that the treatments for Anorexia Nervosa and other Eating Disorders are effective but now wish to investigate the underlying mechanisms to understand for whom and how treatments work. Such knowledge can help clinicians target the right patients and further develop complex interventions for greater benefit. For example the project will address research questions such as “What aspects of the therapy experience bring about improvements in eating disorder symptoms?” and “Which psychological variables should be targeted to restore well-being and healthy weight?”. The COVID pandemic has led to dramatic increases in new eating disorders presenting, as such the questions addressed in this project are more pertinent now than ever.

Professor Daniel Stahl, Dr Ewan Carr and Professor Paolo Fusar-Poli


Support areas: Daniel Stahl (previous and ongoing supervision: 8 PhD, 7 DClinc Psych, 3 NIHR Pre-doctoral Fellows) is Professor in Medical Statistics and Statistical Learning and is the lead of the "Precision medicine and Statistical learning" research group. He is lead of the “Prediction Modelling group” in the NIHR Maudsley British Research Council (BRC). Ewan Carr is Senior Research Fellow at the Department of Biostatistics and Health Informatics, King’s College London. He contributes to several BRC themes including Prediction Modelling and Topological Data Analysis. Prof Paolo Fusar Poli is a Professor of Preventive Psychiatry at the Department of Psychosis, King’s College London, where he heads the Early Psychosis: Intervention and Clinical-detection Laboratory (EPIC Lab). His research focuses early detection of people at risk of psychoses and personalised psychiatry

Based in the Department of Biostatistics and Health Informatics, we offer a methodology training programme in prediction modelling at the Institute of Psychiatry, Psychology and Neuroscience, King's College London. The fellow will be trained as a statistician/data scientist in applied health research by attending the MSc in Applied Statistical Modelling and Health Informatics in our department. Within our collaborative team of statisticians, data scientists, and health informaticians and our close collaborations with clinicians from across the IoPPN, the fellow will have the opportunity to develop dynamic prediction models using clinical electronic health records using the Clinical Record Interactive Search (CRIS) system that has been developed for use within the NIHR Maudsley Biomedical Research Centre (BRC). Dynamic models allow us to predict health outcomes, such as the development of psychosis or recurrence of depression, using repeated measures of relevant predictors over time. Importantly, this approach allows the model to be continuously updated when new longitudinal measurements become available. The fellow will work closely on current research projects in close collaboration with clinicians who will provide expert knowledge in the clinical domain. Existing applications include work in psychosis and antidepressant treatment response.

Dr Kimberley Goldsmith


Support areas: We would like to offer a methodology training programme at the Institute of Psychiatry, Psychology and Neuroscience, King's College London.
The programme will focus on using causal modelling to answer questions of interest in the study of heroin addiction treatments. We have data already with evidence of effectiveness, but we need now to investigate nature and direction of causality. The fellow will attend the MSc in Applied Statistical Modelling and Health Informatics in the Biostatistics & Health Informatics Department to be trained as a data scientist and statistician. The fellow will also have opportunities to study causal questions of interest in large trials of contingency management for addiction, such as ConMan and PRAISe. This would include methods such as mediation analysis to understand how contingency management has effects on important outcomes, and instrumental variable and other methods to study aspects of treatment process and adherence. The results of such analyses will empirically inform targeted treatment refinement. Data on other types of addiction management strategies will also be available to the fellow.

Professor Richard Emsley


Support areas: Richard Emsley is an NIHR Research Professor and Professor of Medical Statistics and Trials Methodology at the Institute of Psychiatry, Psychology and Neuroscience. My research interests are in clinical trials methodology, and developing statistical methods to test whether and how treatments work using causal inference approaches. I develop novel clinical trial designs which aim to answer questions about treatments more quickly and using fewer patients. With clinical colleagues, I apply these methods in randomised trials in mental health conditions. I am keen to supervise people in the area of clinical trials methodology, and part of the supervisory team will include outstanding clinical researchers in mental health, especially psychosis, developmental disorders or self-harm.

Professor Richard Dobson and Zina Ibrahim

Email: and 

Support areas: Richard Dobson's research focuses on the use of data (e.g. omics, electronic health records, smartphones and wearables) to transform the delivery of healthcare by addressing some of the fundamental uncertainties of clinical medicine: How do we diagnose disease and its sub-types? Is this intervention effective? How do we personalise care?

The research has required the extensive use of computational approaches such as machine learning, the creation of software tools, development of a hospital development environments and large private cloud infrastructure to enable integration of patient datasets.

In a team led by Prof Richard Dobson (Department of Biostatistics and Health Informatics), Zina Ibrahim offers methodological training in the theory, application and design of Machine Learning (ML) applications aiming to uncover knowledge from the vast amount of data available within the healthcare ecosystem. In a collaborative team of Informaticians, medical scientists and clinicians, the fellow will have the opportunity to design robust ML-driven applications for the early prediction of adversity in response to treatment, as well as the prediction of hospitalisation outcomes from time-series data housed within electronic hospital records (EHRs). The team has already established a strong body of work in the prediction of hospitalisation outcomes in patients with sepsis, pneumonia and those infected with COVID-19. The projects will also develop unsupervised exploratory algorithms to answer questions such as: “how do subphenotypical traits influence the prevalence of a given outcome in a patient population?” and “how do I design ML pipelines that are explainable and trustworthy for actual use in clinical settings?”

Raquel Iniesta


Support areas: Raquel Iniesta is BRC Senior Lecturer in Statistical Learning at the department of Biostatistics and Health Informatics, King’s College London. She leads the “Topological Data Analysis for Machine Learning working group”.

Based in the Department of Biostatistics and Health Informatics, the fellow will be trained as a statistician in applied health research including prediction modelling, machine learning and topological data analysis by attending the MSc in Applied Statistical Modelling and Health Informatics, and the departmental talks and seminars. Thanks to our collaboration with clinicians from across the IoPPN and King's College London, the fellow will have the opportunity to develop topological machine learning models to identify subgroups of patients of interest (for example good vs bad responders to a drug) using data from clinical trials and clinical electronic health records from the Clinical Record Interactive Search (CRIS) system that has been developed for use within the NIHR Maudsley Biomedical Research Centre (BRC). Topological Data Analysis is a recent and promising field that allows us to extract information from big datasets by inspecting their shape. Existing applications include works in cancer and antidepressant treatment response.The fellow will work closely with mathematicians, computer scientists and clinicians that will provide expert knowledge in the theoretical and clinical domains.

University of Manchester

Rachel Meacock


Support areas:

  • Health economics
  • Economics of healthcare organisation
  • Financing and delivery

Matt Sutton


Support areas:

  • Health economics
  • Economics of health and care

University of Newcastle

Niina Kolehmainen


Support areas: Strong methods expertise in the development and evaluation of complex non-drug interventions in the context of children and families, and access to co-supervisors with complementary clinical, life-sciences and behaviour change expertise as required. 

Mark Pearce


Support areas: Professor of Applied Epidemiology. Broad area of interest within epidemiology, but particularly related to lifecourse studies or paediatric and perinatal research. Lead of the Newcastle Thousand Families birth cohort, established in 1947, and with lots of data for potential statistical analyses. Also lead of the MRes (Epidemiology) programme and the MSc (Public Health) dissertation module at Newcastle University and happy to speak to potential applicants and link to other colleagues.

University of Oxford

Daniel Prieto Alhambra


Support areas: I am Theme Lead for Observational Research at the Centre for Statistics in Medicine, University of Oxford. I have expertise in the analysis and interpretation of routinely collected data including national and international electronic medical records, registries, and audit/s data for research. Please visit my webpage for more details.

University of Sheffield

Ines Rombach


Support areas: Ability to support areas relating to RCT design, analysis and interpretation; analysis of incomplete data and sensitivity analysis for missing data; Trials within cohort studies (TWICS).

Mike Bradburn


Support areas: Ability to support areas relating to generalisability/external validity of RCTs.

Professor Stephen Walters


Support areas: Ability to support areas relating to Design, conduct, analysis, and reporting of trials of complex interventions. Design, assessment, analysis and interpretation of patient reported outcomes in clinical trials and cluster randomised controlled trials.

Nicholas Latimer


Support areas: Nicholas Latimer is a Professor of Health Economics based at the School for Health and Related Research, University of Sheffield. His research interests include:

  • Survival analysis in the context of health technology assessment
  • Causal inference methods to estimate comparative effectiveness from real world data sources

Professor Steven A. Julious


Support areas: I am a Professor of Medical Statistics and a NIHR Senior Investigator. I have supported successful applications to both the NIHR Pre-doctoral and Doctoral wards and enjoy working with career young researchers.

I am an applied medical statistician and have the ability to support areas relating to clinical trials, study design, sample size calculations, meta-analysis and asthma epidemiology.

I am also the lead for the MSc in Statistics with Medical Applications which, depending on the candidate, could form part of the training programme for the award.

I am also located within the school of health and related research (ScHARR) which has an active research environment across disciplines. If successful the fellow will be able to work with and collaborate with statisticians, health economists, data managers and clinical scientists within the school.

Aki Tsuchiya


Support areas: Aki Tsuchiya is a Professor of Health Economics based at the University of Sheffield, with a joint appointment between the Department of Economics at the School of Health and Related Research. She is a co-Director of the Centre for Wellbeing in Public Policy at the University, and a member of the EuroQol Group. She has extensive experience teaching health economists to economists at both the undergraduate and postgraduate levels, and supervising research students.

Aki’s methodological research interests include:

  • measuring, valuing, and modelling health and wellbeing outcomes, and inequality aversion
  • incorporating equity concerns into social welfare functions
  • normative economics of health, public health and beyond.

University of Southampton

Diana Baralle


Support areas: I am a clinical academic in genetics and genomics. I work on finding new disease gene associations for rare diseases and deep phenotype of them. My lab is involved in bringing RNA and splicing into diagnostics using wet lab, RNA seq and bioinformatics, expanding on functional genomics. I have also used RNAseq in COVID, looking for biomarkers of disease, severity and response to treatment.

University of Warwick

Professor Sian Taylor-Phillips


Support areas: I specialise in evaluating population screening programmes, such as the NHS cancer screening programmes (Breast, Cervical and Bowel), newborn blood spot screening and antenatal screening. I evaluate proposed new screening programmes and screening tests, through primary research such as observational studies and randomised controlled trials, and through systematic review of the published literature. My particular specialism is breast cancer screening.

Dr Lazaros Andronis


Support areas: I'll be able to support prospective pre-doctoral students interested in topics related to:

  • Methods of economic evaluation in health care (with a particular interest in the evaluation of interventions targeting children and young people)
  • Measurement of costs borne by patients and their families (with a special interest in the value of time forgone due to seeking or receiving care)
  • Identification and measurement of patients' preferences for 'process' outcomes (e.g. continuity of care, access, convenience etc).

I'm happy to engage with prospective students to formulate research questions tailored to their particular interests and advise on suitable training and development plans.

University of York

Rowena Jacobs


Support areas: I am a health economist with an interest in the economics of mental health. My expertise is in the econometric analysis of routine, administrative, survey and linked data. I can supervise on topics related to:

  • mental health policy reforms, incentives and performance measurement
  • funding and contracting approaches for mental health services
  • economic impact of mental health and labour market effects
  • socio-economic determinants of mental health
  • mental health and multimorbidity

Laura Bojke


Support areas: I am a health economist with an interest in public health economic evaluation and health technology assessment. I can supervise on topics related to:

    • Applied evaluation of public health interventions/ programmes/policies
    • Development of frameworks or methods to evaluate public health interventions/programmes/policies
    • Methods to evaluate nature based/environmental interventions
    • Economic evaluation to inform local level decision making
    • The use of routine data for economic evaluation

      Noemi Kreif


      Support areas: Health econometrics, Policy impact evaluation, Causal Inference and Machine Learning

      Nils Gutacker


      Support areas: I’m a health economist with interests in economic issues around the supply of care, including recruitment and retention of the healthcare workforce, the impact of market structures, and the optimal design of incentive schemes.

      Adriana Castelli


      Support areas: Health system performance, efficiency, workforce, digital technology

      Peter Sivey


      Support areas: Health economics, economics of health care services, demand and supply in health care, waiting times, emergency care performance, markets in health care