Primary Supervisor – Dr. Matthew Jones

Scientific background

Megafires, characterized by their extraordinary size, speed, and intensity, increasingly threaten society, ecosystems, and ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide new opportunities to study extreme fires on a global scale. In a changing climate, conditions prone to megafires could become more prevalent ( 3 – 4 ). However, the main mechanisms that promote or prevent megafires remain poorly studied in most regions globally.

This project addresses critical knowledge gaps by combining new observations of individual fires globally (5) and climate datasets with machine learning to predict the occurrence of megafires. The successful candidate will contribute to pioneering efforts to forecast wildfire risk and identify land management or policy factors that have the potential to mitigate these risks.

Research questions

• Are megafires becoming more frequent globally, and in what regions?
• What weather, landscape, and land use factors promote or inhibit the development of wildfires?
• Has climate change increased the risk of wildfires, and how might these risks evolve in the future?

methodology

With the support of the supervisory team, the researcher will:

• Develop a comprehensive global dataset of individual fires, compiling meteorological and landscape variables that have the potential to influence megafire development, based on the Global Fire Atlas (4).
• Identifying megafires: Distinguishing regionally between megafires and “typical” fires with a lower potential for catastrophic impact.
• Diagnosing wildfire-prone situations: Harnessing machine learning techniques to identify key factors that promote/prevent wildfires. Untangling the roles of weather, landscape, and human factors that influence ignition and suppression.
• Analysis of regional trends in megafire probabilities: Study of regional trends in observed megafire occurrence (since ~2000) and megafire-prone weather (since ~1980s), with the opportunity to contribute to major reports on this topic (2,4).

Training and development

The training will increase future employability in academia and industry:

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

Person specifications

Highly motivated filter with:

• Degree or equivalent in numerical, computational or environmental fields.
• It is recommended to try programming languages ​​such as Python or R to analyze scientific data.

More information: mattwjones.co.uk/research-team-and-open-positions.

Entry requirements

At least a UK BA (Hons) equivalency of 2:1. English language requirements (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).

Acceptable first degree:

• mathematics
• statistics
• Physics
• Economy
• finance
• Engineering (mechanical, electrical, civil, etc.)
• Data science

Start date

October 1, 2026

Finance

ARIES Scholarships are governed by UKRITerms and Conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded scholarship, which covers fees, a maintenance stipend (£20,780 per annum for 2025/26) and a Research Training and Support Grant (RTSG). A limited number of scholarships are available to international applicants, with the difference between ‘national’ and ‘international’ fees waived by the registered university. However, please note that ARIES funding does not cover additional costs associated with moving to and living in the UK, such as visa costs or additional health fees.