2020 applicants
Postgraduate Research Opportunity

Population Evidence and Data Science


Title: HSC01: Population Evidence and Data Science

Funding: A full Home/EU fee waiver or equivalent fee discount for overseas students  (£5,103 in 2020-21) (further fee details - international students will need to pay the balance of their fees) plus a doctoral stipend equivalent to the RCUK Minimum Doctoral Stipend (£15,285 in 2020-21).

Application deadline: 5 June 2020

Start date: October 2020

Duration: 3 years (full time)

Location: Colchester Campus

Based in: A department in the Faculty of Science and Health or the Faculty of Social Sciences, depending on lead supervisor.


Applications are invited for a fully funded PhD studentship from October 2020 for three years (full time study). The PhD studentship is linked to the Population Evidence and Data Science Theme of the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) East of England (EoE).

The studentship will be offered to an outstanding early stage researcher to conduct applied research relevant to Public Health and health inequalities using population health data. The studentship is an exciting career opportunity for an ambitious researcher who is keen to develop as a future leader in applied research and data science.

The NIHR ARC EoE is a 5-year collaboration between Cambridge and Peterborough NHS Foundation Trust (CPFT) and the Universities of Cambridge, East Anglia, Essex, and Hertfordshire, along with other NHS trusts, transformation (STPs) partnerships, charities, industry, and patient led organisations and partners across the region.

Suggested topics and areas of study

PhD research questions and proposed programmes of work must align with the NIHR ARC EoE  Population Evidence and Data Science theme priorities (outlined below) and be of relevance to the ARC’s four ‘populations in focus’ (Great Yarmouth and Waveney, Peterborough and Fenland, Stevenage, and Thurrock), which are socioeconomically deprived areas with high health needs.

Also of interest are proposals which cross-cut with other EoE ARC themes, such as mental health across the life course and multimorbidity and ageing.

The Population Evidence and Data Science theme will extend and enhance the application of, the evidence base on the use of data to understand population health needs and outcomes.

Core areas of work are:

  • Innovative methods: identifying, developing and sharing innovative methods relating to population health data access and use;
  • Data linkage: supporting and influencing the establishment and use of linked data sets to inform population health research;
  • Vulnerable populations: developing the evidence base on vulnerable populations at risk of poor health with a focus on the use of population health data to improve outcomes.

Projects that will identify key learning to aid local services in implementation and application of linked data to improve health and reduce health inequalities are a priority.

Examples of potential topic areas include:

  • Does the supplementation of survey data by linked admin data enhance our understanding of health in vulnerable populations?
  • Using data to characterise coastal and rural populations: understanding health and social inequalities.
  • Vulnerable families:  health, social and economic impact of diagnosis of long term conditions on household members.


The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,285 in 2020-21), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.


Applicants are encouraged to identify a possible principal supervisor in the University of Essex, with a list of potential supervisors provided below.

The successful candidate will also have a co-supervisor from a different discipline and/or School within the University. This will be arranged once the successful candidate has been decided and their research topic confirmed.



  • Applicants should have a 2.1 or higher Honours degree in a relevant discipline.
  • A good knowledge of health or social care, and an interest in applied research relevant to a specific aspect of public health, population health management, health inequalities and/or data linkage.
  • Applicants must demonstrate how they will add value to NHS/health, social care or other provider organisations.


  • An MSc in a relevant discipline.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement (maximum 500 words), and transcripts of UG and Masters degrees in your application.

You will also need to submit a research idea or project proposal (maximum of 1000 words) that explicitly addresses the work you would like to carry out, detailing how it is linked to the ARC Population Evidence and Data Science theme; whether it cross-cuts with other ARC EoE themes; how it will mobilise population health data and/or make secondary use of available datasets. 

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "Public Health", tick "postgraduate research" and hit "search". This will bring up a list of matching courses, select the one marked "full time" and "PhD".
  • For "Proposed research topic or area of research" please put the title of this studentship (HSC01: Population Evidence and Data Science)
  • For "If you have contacted a potential supervisor..." please put the potential supervisor that you have contacted.

If you have any informal queries about this opportunity please email Professor Meena Kumari (mkumari@essex.ac.uk).