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Andrew Cunliffe

6 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

Full funding available

Deadline

Expired

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Country

United Kingdom

University

University of Exeter

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Keywords

Machine Learning
Ecology
Environmental Science
Geography
Spatial Analysis
Ecological Restoration
Plant Production
Physical Geography

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

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

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