Andrew Cunliffe
3 months ago
Fully Funded PhD in Geospatial Ecology and Environmental Science at University of Exeter University of Exeter in United Kingdom
Degree Level
PhD
Field of study
Machine Learning
Funding
Fully funded for UK nationals: domestic tuition fees covered, £20,780 tax-free stipend per year for 3.5 years, plus £15,000 research training support grant.
Deadline
Expired
Country
United Kingdom
University
University of Exeter

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About this position
The University of Exeter’s Department of Geography is offering a fully funded PhD opportunity in geospatial ecology, focusing on evaluating the influence of NEOM regreening approaches on terrestrial productivity. The successful candidate will join the Terrestrial Ecosystem Science and Services (TESS) Lab, led by Associate Professor Andrew Cunliffe, and work alongside Professor Ted Feldpausch. The project aims to deliver new understanding of plant productivity variability in response to dryland management and environmental variation, leveraging advanced mapping, spatial analysis, and machine learning techniques.
The NEOM project represents one of the largest ecological restoration initiatives globally, targeting regreening of drylands across vast landscapes. This PhD will build on the Relative Productivity Index (RPI) framework, using observed versus potential productivity modelled with machine learning, to study how vegetation productivity varies and responds to management interventions. The research will contribute to evaluating the effectiveness of NEOM’s regreening efforts and advance knowledge in ecological restoration, geospatial ecology, and environmental science.
The TESS Lab is an inclusive, supportive environment with regular group meetings, mentorship, and collaboration opportunities. The lab integrates social science to promote sustainability and works with diverse partners to maximize research impact. The PhD studentship is fully funded for UK nationals, covering domestic tuition fees, a tax-free stipend of £20,780 per year for 3.5 years, and a £15,000 research training support grant. The position starts in April 2026, and applicants should have a strong background in geography, environmental science, ecology, or related fields, with experience in geospatial analysis, remote sensing, or machine learning preferred.
For full details and to apply, visit the provided application link. The deadline for applications is midnight, 10th January 2026.
Funding details
Fully funded for UK nationals: domestic tuition fees covered, £20,780 tax-free stipend per year for 3.5 years, plus £15,000 research training support grant.
What's required
Applicants should hold or expect to obtain a first or upper second class degree in Geography, Environmental Science, Ecology, or a related discipline. Experience with geospatial analysis, remote sensing, or machine learning is desirable. Strong quantitative skills and interest in ecological restoration and dryland management are preferred. UK nationals are eligible for domestic tuition funding.
How to apply
Visit the provided application link for full details and to apply. Prepare your academic CV and supporting documents. Submit your application before the deadline. Contact the supervisors for informal inquiries if needed.
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