Probabilistic machine learning methods for blending weather forecasts towards optimal health risk prediction UNRISK NERC Centre for Doctoral Training PhD studentship 2026/27 Entry. Ref: 5719

Supervisors

Primary Supervisor
Professor Theo Economou

Institution

University of Exeter (Mathematics and Statistics)

Academic Supervisors

Dr Ben Youngman

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Background

Temperature records have been broken repeatedly in recent decades and heat-stress is increasingly a serious health risk (e.g. 2007 London marathon fatalities). This project aims to quantify the spatially and temporally varying heat-risk in the UK using statistical models and to understand how weather forecasts and climate projections can be leveraged for informing adaptation policy and mitigation action such as early warnings. Interpretable probabilistic machine learning (ML) methods will be used to model weather and health data available at the Met Office so the sits at the interface between ML, environmental science, meteorology and epidemiology. Skills such as data analysis and modelling, environmental and health data manipulation, risk mapping and decision making under uncertainty are expected to be gained by the student, who will have the chance to be hosted at the Met Office as a visiting scientist.

PhD Opportunity

The first challenge is the question of how to best use statistical machine learning methods for understanding the relationship between heat-stress and health outcomes. Heat-stress is a combination of unfavourable temperature, humidity and wind-speed over a generally unknown number of hours/days etc. Appropriate data modelling tools will need to be utilised to understand health-risk as a function of heat-stress, allowing for socio-economic factors of the population-at-risk, in addition to the inherent spatio-temporal variability.

Next, is the question of how to use the estimated risk in mitigation and adaptation strategies. For mitigation, prescriptive (decision making) approaches for issuing health warnings will be investigated. For adaptation, future climate projections of heat-risk will be computed and contrasted for various climate change scenarios and climate models. Statistical machine learning methods will be used to probabilistically quantify the uncertainty from the various sources of climate projections, enabling their use in decision making.

A key challenge is the question of how to optimally use the available weather and health data for quantifying the health risks and whether blending various data sources is beneficial for reliable forecasting.

Expertise and data from the Met Office (MO) will be available towards tackling these challenges, noting that the current ‘heat-health alert service’ is co-managed by the MO.

Applicant Profile

Students with a strong background in statistics and/or machine learning who want to apply these skills to environmental epidemiology and more generally the interface between climate change and health

Other information

https://experts.exeter.ac.uk/19235-theo-economou
https://experts.exeter.ac.uk/43970-christophe-sarran
https://www.metoffice.gov.uk/weather/warnings-and-advice/seasonal-advice/heat-health-alert-service
https://www.metoffice.gov.uk/research/climate/climate-impacts/health

Funding

UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them tuition fees and a stipend for a period of 3 years 9 months which includes the expectation that PhD students will undertake a placement and also take part in cohort based residential research events. Further up to date information about studentship funding is available from UK Research and Innovation.

Applications are open to UK and international applicants, although the number of awards for international applicants is limited by UKRI rules. Please note that the grant cannot cover visa and NHS International Health Surcharge (IHS) costs, which are in the order of £3000-£4000 to allow overseas students entry to study in the UK.

Some additional places are also available for students who have their own funding, such as scholarships, and whose research is closely aligned with UNRISK.  

Part-time study can be offered to students unable to join the programme as a full-time student. Please get in touch with us if you would like to discuss this option. 

If you have any questions about the application process please email ENV-PGR@leeds.ac.uk.

Entry requirements

Academic Entry Requirements

You should hold a first or upper-second class Bachelor’s degree or a taught Master’s degree in an appropriate subject from a UK university. Non-UK qualifications of an equivalent standard are also accepted.

Residency

The UNRISK CDT studentships are available to UK and International applicants. Following Brexit, the UKRI now classifies EU students as international unless they have rights under the EU Settlement Scheme. The GW4 partners have agreed to cover the difference in costs between home and international tuition fees. This means that international candidates will not be expected to cover this cost and will be fully funded but need to be aware that they will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. All studentships will be competitively awarded and there is a limit to the number of International students that we can accept into our programme (up to 30% cap across our partners per annum).
 

English Language Requirements

If English is not your first language you will need to meet the English language requirements by the start of the PhD programme. This will be at least 6.5 in IELTS or an acceptable equivalent. Please refer to the relevant university for further information. Please refer to the English Language requirements web page for further information.

How to apply

You must apply for funding via the University of Leeds website further details can be found here Application Process – Understanding Uncertainty to Reduce Climate Risks

Please note that only those applications submitted directly via the University of Leeds application system will be assessed for funding. Applying directly to your chosen programme of study at the University of Exeter will not constitute an application for funding.

Data Protection

If you are applying for a place on a collaborative programme of doctoral training provided by University of Leeds and other universities, research organisations and/or partners please be aware that your personal data will be used and disclosed for the purposes set out below.

Your personal data will always be processed in accordance with the General Data Protection Regulations of 2018. University of Leeds (“University”) will remain a data controller for the personal data it holds, and other universities, research organisations and/or partners (“HEIs”) may also become data controllers for the relevant personal data they receive as a result of their participation in the collaborative programme of doctoral training (“Programme”).

Further Information

For an overview of the UNRISK NERC CDT programme please see the website https://unrisk-cdt.ac.uk/

Summary