Data Science Modelling of Infection and Antibiotic Resistance Using NHS Big Data and Machine Learning to Improve Healthcare and Therapeutics - Computer Science - EPSRC DTP funded PhD Studentship Ref: 2950

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Jacqueline Christmas
Dr. Robert J Porter

Streatham Campus, Exeter

Project Description
There is a wealth of data available to NHS Doctors, and we need to use it intelligently in order to treat a rising tide of infectious disease. There has been an 18% increase in blood stream infections with E. coli in England between 2013 and 2016, with each episode carrying a mortality risk of 15%. The UK government has set a target for a 50% reduction in all Gram negative blood stream infections by 2020. Predominantly urinary in origin, and beginning mainly in non-hospitalised patients, preventing these infections requires pre-hospital intervention. Resistance to antibiotics is increasing, making appropriate choice imperative for patient outcomes. 

NHS laboratories routinely collect results and we have access to large datasets spanning a decade from three major hospitals in the South West Peninsula region. The primary goal is to model the risk of patients having an antibiotic resistant organism, before their laboratory test is complete, to allow immediate treatment with effective antibiotics. Predicting resistance and sensitivity patterns based on previous results is a powerful mechanism for ensuring general practitioners can prescribe the most effective antibiotic. We foresee the development of novel approaches, such as pre-hospital intramuscular antibiotic delivery, in those carrying multiple risk factors for poor outcome.

The successful applicant will work in a multidisciplinary team, interacting with academics, clinical scientists and medical doctors. This PhD scholarship will suit someone with experience in data science and machine learning who wishes to apply their skills on real life datasets, to deliver practical and clinically relevant results. The PhD studentship will require use of large datasets and good programming skills are essential. This funded PhD will involve developing analytic approaches to provided data, then sharing the results with clinical Doctors in order to further refine and develop the methods. Familiarity with effective data presentation methods will therefore be crucial.

Deep analysis of the available data will be conducted to test hypotheses, guide optimal treatment strategies, and monitor results of our tailored interventions. We will develop a core set of tested analytics that can be implemented with ease. Subsequently a robust analysis toolkit will be produced for use by medical doctors all around the region. 
This is the cutting edge of medical practice and will lead to multiple opportunities for publication and presentation of work. Can we use NHS ‘big data’ to predict patients at risk of treatment failure,  allowing early and effective therapy?

Entry Requirements
The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       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.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

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

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.