Clinical data science: Developing a patient trend monitoring tool
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
October and February
Applications accepted all year round
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
The work on this project could involve:
- Data science analysing anonymised clinical data from hospital patients
- Contributing to a study furthering medical knowledge or hospital efficiency
The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group. In collaboration with Portsmouth Hospitals and others, our work in clinical outcome modelling has supported the development of the VitalPAC vital signs collection system and the National Early Warning Score (NEWS) recommended by the Royal College of Physicians and mandated by the NHS, among many other projects.
We are looking for high quality numerate graduates who wish to develop their data science skills on applications that may have rapid health service adoption. We work closely with our clinical partners to understand the ways in which their data is collected and how it can best be used to promote better outcomes for patients and better efficiency for the clinicians (e.g. doctors and nurses) involved and their organisations (e.g. hospitals).
We are particularly interested in work that monitors trends in a patient's condition over time, and identifying the appropriate point at which to intervene. Alternatively, we are broadening the scope of our work beyond its origins in general patient deterioration to other more specific medical areas, including surgery, intensive care and emergency medicine.
The successful candidate will join a team of academics, research staff and other PhD students who work closely with NHS clinicians and data scientists. We have excellent computing facilities and a friendly and supportive working environment.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a**
Home/EU/CI part-time students: £2,250 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
High level of numeracy and confidence in dealing with data analysis. Previous health sector experience is not essential.
How to apply
We’d encourage you to contact Prof Jim Briggs (Jim.Briggs@port.ac.uk) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, please follow the 'Apply now' link on the Health Informatics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
When applying please quote project code: COMP5471021