About this role
Primary supervisor - Dr Matthew Jones
Secondary supervisor - Prof Timothy Osborn
Other supervisors - Dr Calum Cunningham , Prof Crystal Kolden , Dr Marco Turco
Extreme wildfires are becoming more prominent across many regions, yet the underlying causes and expressions of these events shows high variability. In some biomes, such as boreal, mediterranean, and temperate conifer forests, fire seasons appear to be lengthening and intensity increasing. In others, including tropical forests in Brazil and the Congo, large surges in fire counts have occurred during drought years. Whether these trends share common drivers or reflect distinct syndromes of fire ignition and behaviour remains understudied.
This PhD will investigate how and why extreme wildfire activity is changing across tropical and extratropical forests. Using global Earth observation and climate datasets, the student will identify different syndromes of extreme fire and assess the climatic, ecological, and human factors that give rise to them.
The project will progress through three phases:
Classification and diagnosis: What forms do extreme wildfires take in different environments? The student will identify and classify fire syndromes using metrics such as size, intensity, rate of spread, and societal impact.
Explanation and prediction: Why do these syndromes occur where and when they do? The student will develop statistical and machine learning models to (i) explain the occurrence of extreme fires and (ii) predict their likelihood under present and future climates, testing how drivers differ across regions and how early extremes can be predicted.
Application and impact: How can early-warning insights help strengthen preparedness and resilience? The student will organise a major international summit of stakeholders from science, policy, industry, and land management, targeting a co-developed set of priorities for a policy- and society-relevant wildfire research agenda.
The student will join the State of Wildfires Project (https://stateofwildfires.com/) at UEA, receiving advanced training in satellite data analysis, machine learning, and climate and fire modelling, with opportunities to collaborate internationally across science, policy, and industry.
Entry requirements
The minimum entry requirement is 2.1 in any quantitative and analytical subject area is acceptable but a passion for the topic area is critical. Examples include: Mathematics, Statistics, Physics, Data Science, Physical Geography, Environmental/Biological Sciences, Economics, Finance, Engineering, etc.
Start date: 1 October 2026
Additional Funding Information
This PhD project is fully funded by the Leverhulme Trust and the University of East Anglia for 42 months. Funding includes tuition fees at home-fee rate, an annual tax-free maintenance stipend set at the UKRI rates (£20,780 for 2025/26), and an annual budget to support research training.