University of Exeter funding: PhD: Research England DTP Studentsh

Exploiting observational data in the design and analysis of clinical trials into effective diabetes treatment. PhD in Medical Studies (Research England DTP) Ref: 3753

About the award


Lead Supervisor

Prof Jack Bowden, College of Medicine and Health, University of Exeter

Additional Supervisors

Dr  Beverley Shields, College of Medicine and Health, University of Exeter

Dr Lauren Rodgers,College of Medicine and Health, University of Exeter

Location: University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW

Due to a major recent award, applications are invited from students wishing to further their scientific careers by undertaking a PhD in a diabetes related area of research. Up to four studentships will be fully funded from autumn 2020 with enhanced stipends funded from a new £6 million award. This award reflects Exeter as a world renowned centre of excellence for diabetes research.

Students can select from any of the advertised four projects. These projects have been carefully selected to provide students with an excellent scientific training in an important area of diabetes research, the latest laboratory and computing skills, outstanding resources, and with world leading scientists as supervisors. They cover various aspects of diabetes research, including autoimmunity in the pancreas; neuro-endocrinology to understand the relationship between the brain, mental health and the endocrine system; gene regulation in the placenta and fetal development of the pancreas; rare genetic forms of diabetes; muscle physiology; and the use of electronic medical records to understand disease causes, treatments and progression. Students will learn a wide range of state-of-the-art techniques, which could include CRISPR-Cas9 gene editing, DNA methylation, DNA sequence analysis, muscle insulin sensitivity physiology, brain electrophysiology, medical statistics, R for statistics and data visualisation and programming in python, data science including machine learning, in vivo metabolic phenotype skills and cell biology including 3D stem cell culture. Students will have access to outstanding resources, including cohorts of >5000 patients with rare defects in insulin secretion, a world leading collection of samples for study  of pancreas pathology, resources of electronic medical records and biobanks from millions of people and unique resources for studying human development of the pancreas and brain.

Funding Notes

This is a 3 year fully-funded PhD studentship. Stipends are at an enhanced rate of £17,059 (2020-21) and all Home/EU tuition fees are covered. Funds will also be available for travel and research costs.

Project Summary

This studentship will develop novel methods to exploit observational study and patient record data to improve the design and analysis of clinical trials into Diabetes. The student will develop strong quantitative skills in Statistics & data science, as well as state-of-the-art knowledge of Diabetes, its causes consequences, and its effective treatment.

Project Description

In diabetes, there are many different treatment options, but little guidance regarding which drug works best for which individuals.  As part of our precision medicine research, we are developing statistical models to help determine the optimal treatment for diabetes patients based on their clinical features such as age, BMI and blood biomarkers.

To develop these models, we have been using data from large GP records databases and randomised controlled trials (RCTs), but both have limitations. In RCTs, patients tend to reflect a narrow, selected subgroup and not all patients adhere to the study protocol or may drop out before study completion. This hinders the interpretation and generalisability of a trial’s findings in the outside world.  In contrast, observational data such as GP records have real-world relevance, but the data are messy, and many variables must be controlled for to obtain estimates comparable to those from an RCT.

Despite these challenges, there is a growing interest in developing statistical methods to combine both data sources, in order to improve patient care. This PhD will explore three specific aims: 

1) To develop causal inference approaches for combining observational and RCT data to improve treatment effect estimation in a trial.
2) To use these findings to further refine our treatment prediction algorithms.
3) To test updated algorithms in trial settings, incorporating real world data to further improve trial analysis and better reflect routine practice.

This PhD will offer the student the opportunity to work with leading statisticians in trial methodology and a world-renowned diabetes research team.

The student should have a solid grounding in Statistics and experience in handling and analysing medical data. The student will join a vibrant, diverse and world class interdisciplinary research team, including geneticists, biological scientists, mathematicians, computer scientists and clinicians, all studying aspects of diabetes and related conditions.

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

Applicants must ensure that they meet the eligibility requirements of the University of Exeter.  To qualify for ‘home’ tuition fee status, you must be a UK or EU citizen who has been resident for 3 years prior to commencement.

All applicants would need to meet our English language requirements by the start of the  project

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

- A two page CV with details of your qualifications, any relevant experience and skills.

- Covering letter – Please include, within the maximum word limits, the following
    i) a description of any prior research experience and skills (200 words)
   ii) your reasons for wishing to do a PhD (100 words), and
  iii) the reasons for your project selection – please select a maximum of three and rank in order of preference (300 words)

- Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.

- If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English.

- Two References (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school).

Reference information
If you do not upload your references when submitting your application, it is your responsibility to ensure that your two referees email their references to, as we will not make requests for references directly; you must either upload them with your application or arrange for them to be submitted by Monday 2 December 2019.

Please note: References should be submitted to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is midnight on 2nd December 2019.  Interviews will be held the week commencing 13th January 2020.

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the supervisor.

Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs; administrative staff at one or more other HEIs participating in the relevant programme.
Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.


Application deadline:2nd December 2019
Value:For eligible students, this 3 year funded studentship will cover Home/EU tuition fees and an enhanced stipend of £17,059 (2020-21). Funds will also be available for travel and research costs.
Duration of award:per year
Contact: PGR Recruitment Office