Statistical AI approaches for optimal health warning systems. Department of Mathematics and Statistics,UQ-Exeter Institute PhD Studentship (Funded) for January 2027 Entry. Ref: 5843

Join a world-leading, cross-continental research team

The UQ Exeter Institute is seeking exceptional students to join a world-leading, international research partnership tackling major challenges facing the global community in sustainability and wellbeing. Our joint PhD program provides a fantastic opportunity for the most talented doctoral students to work closely with world class research groups and benefit from the combined expertise and facilities at The University of Queensland and the University of Exeter. This prestigious program provides full tuition fees, stipend, travel and development funds and Research Training Support Grants to the successful applicants.

This select group of high-calibre doctoral candidates will have the chance to study in the UK and Australia, and will graduate with a joint PhD degree from The University of Queensland and the University of Exeter.

The studentship provides funding for up to 42 months (3.5 years).

Find out more about the PhD studentships www.exeter.ac.uk/quex/phds

Successful applicants will have a strong academic background and track record to undertake research projects based in one of the four priority themes.

Successful applicants will undertake this joint program on a full-time and onshore basis, commencing in Australia (UQ-homed) or in the UK (Exeter-homed). At least 12 months will be spent at each institution over the period of the joint PhD program.

The closing date for applications is midday Friday, 24 April 2026 (BST), with interview to be held between Monday, 25th May and Wednesday, 3rd June 2026. 

The start date is expected to be Monday, January 4th January 2027.

Please note that of the eight Exeter led projects advertised, we expect that up to four studentships will be awarded to Exeter based students.

Theme: Healthy Living

Supervisors:

Exeter – Associate Professor Theo Economou

UQ – Associate Professor Nicholas Osborne

Project Description

Temperature extremes and poor air quality are two of the main hazards humans are exposed to, particularly in urban environments where over 55% of population reside and rising. Although individually both temperature extremes (e.g. heat-stress) and air quality (e.g. Nitrogen Oxide from car fumes) are known to severely impact human health, it is unclear how these stressors interact although it is expected that their effect is compounded. The hazard to human health is therefore not well-understood, where individual assessments of either temperature or air quality are likely to underestimate the health risk.

It is therefore not surprising that most health early warning systems are designed for either temperature extremes or air pollution but not their synergy. At the same time, there is a lack of a coherent and mathematically rigorous methodology for how health, environmental and population exposure and vulnerability data can be combined to optimally issue warnings in order to minimise health risk. 

The aim for this project is to investigate the use of statistical AI methods for a) estimating the synergistic effect of temperature, humidity and air quality on human health (mortality and morbidity); b) to propose a prescriptive framework for using these estimates to optimally issue health warnings and c) to investigate the implications of climate change to the compound risk from environmental extremes.

The project will use state-of-the-art statistical AI approaches in environmental epidemiology (a unique strength of the Exeter-Queensland team), to quantify the degree to which the effects from temperature, humidity and air quality on human health are compounded, while also allowing for population characteristics (age, sex, socio-economic background) and exposure. The project will also use ideas from decision theory to investigate approaches for optimally issuing warnings with a view to minimise health risk to the respective sections of the population. Moreover, the project will utilise the latest data from climate models which contain information for how the climate, the population and its vulnerability/exposure are projected to change under various socio-economic scenarios. The information in these data sets and the estimated health risks as a function of environmental extremes will be combined to produce predictions of future health risk under different climate scenarios. 

The project is expected to produce new knowledge into the synergistic effects of weather and air quality extremes, the way the estimates health risks might change in warming planet and present a health warning framework with which this knowledge can be used to mitigate health risk. The project partners, Public Health Scotland and the Met Office, will offer unique knowhow in terms of health surveillance, operational risk management and data access as well as insights regarding societal relevance and potential adoption and piloting of the resulting warning system.

Contact

Questions about this project should be directed to Associate Professor Theo Economou at T.Economou@exeter.ac.uk

Entry requirements

Applicants should be highly motivated and have, or expect to obtain, either a first or upper-second class BA or BSc (or equivalent) in a relevant discipline.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

How to apply

Apply now

To apply for this studentship project please use the 'Apply now' button above.  Important note:  If you apply for this project via a different route your application will not be considered. 

You will be asked to submit some personal details and upload the following documents:

• a full CV
• A Personal Statement.  Please use the following form. UQ-Exeter Institute Personal Statement.  Please note: the document will open as read only so please ensure you save a copy onto your desktop to edit the document.  Please ensure you upload the completed document to your application.
• academic transcripts and degree certificates
• details of two academic referees.
• English Language qualification.

Please quote reference 5843 on your application and in any correspondence about this studentship.

Summary