PhD Studentship - Predicting and Mitigating Megafires under Climate Change, CASE project with WTW

Primary Supervisor - Dr Matthew Jones

Scientific Background

Megafires, characterised by their extraordinary size, speed, and intensity, are increasingly threatening society, ecosystems, and ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate, megafire-prone conditions could become more prevalent (3-4). However, the key mechanisms that promote or inhibit megafires are under-studied for most regions globally.

This project addresses critical knowledge gaps by combining novel observations of individual fires globally (5) and climate datasets with machine learning to predict megafire occurrence. The successful candidate will contribute to a ground-breaking efforts to forecast megafire risk and identify land management or policy factors with potential to mitigate that risk.

Research Questions

• Are megafires becoming more frequent globally, and in which regions? 
• Which weather, landscape and land use factors promote or inhibit megafire development? 
• Has climate change increased megafire risk, and how could those risks evolve in the future? 

Methodology 

Supported by the supervisory team, the researcher will: 

• Develop a comprehensive global dataset of individual fires, compiling meteorological and landscape variables with potential to influence megafire development, building on the Global Fire Atlas (4). 
• Identify megafires: Regionally distinguish between megafires and more ‘typical’ fires with less potential for catastrophic impact. 
• Diagnose megafire-prone conditions: Harness machine learning techniques to identify key factors promoting/inhibiting megafire. Disentangle the roles of weather, landscape, and human factors influencing ignition and suppression. 
• Analyse regional trends in megafire potential: Study regional trends in observed megafire occurrence (since ~2000s) and megafire-prone weather (since ~1980s), with opportunity to contribute to major reports on the topic (2,4).

Training and Development

Training will maximise future employability in academia and industry: 

• Programming and geospatial data analysis using Python/R. 
• Machine/deep learning techniques. 
• Communication of scientific findings through publications and conferences. 

Person Specification 

A highly motivated candidate with: 

• A degree or equivalent in numerate, computational, or environmental subject areas. 
• Experience with programming languages such as Python or R for scientific data analysis is desirable. 

Further Information: mattwjones.co.uk/research-team-and-open-positions. 

Entry Requirements

At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category). 

Acceptable first degree:

• Mathematics
• Statistics
• Physics
• Economics
• Finance
• Engineering (Mechanical, Electrical, Civil, etc.)
• Data Science

Start Date

1 October 2026

Funding

ARIES studentships are subject to UKRI terms and conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£20,780 p.a. for 2025/26) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.

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