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.

The University of Exeter is offering a fully funded PhD studentship through the EPSRC Doctoral Landscape Award, focusing on quantifying forest plantation damage after cyclones using advanced Earth Observation and AI-driven computer vision techniques. The project addresses the urgent global challenge of rapidly assessing storm damage in forest plantations, with a particular emphasis on New Zealand and the UK, where forestry is a major economic sector vulnerable to extreme weather events. Supervised by Milto Miltiadou and a team including Andrew Cunliffe, Sareh Rowlands, Steven Palmer, Susana Gonzalez Aracil, and Todd Redpath, the research will develop scalable methods that integrate pre-storm LiDAR, post-storm SAR, LiDAR, and optical satellite data. The aim is to enable rapid, safe, and accurate quantification of forest damage, supporting industry partners and contributing to forestry resilience and Net Zero ambitions. Potential research techniques include spatio-temporal AI models (such as LSTMs and transformers like ST-ViT), statistical baselines (ARIMA, Kalman filters), fine-tuning foundation models (e.g., Cambridge TESSERA, IBM–NASA Prithvi), agent-based routing tools using PostGIS, and interactive visualisation tools for decision-making. The project is highly interdisciplinary, combining geospatial ecology, computer science, environmental science, and applied AI. Applicants should have a strong academic background in a relevant discipline (environmental science, computer science, geography, or related fields), with experience in AI, computer vision, remote sensing, or geospatial data analysis. Programming skills and an interest in applied, impactful research are highly desirable. The studentship covers full UK tuition fees and provides a generous stipend, with additional support for research costs and training. International applicants may be eligible for fee waivers or partial support and must meet English language requirements. The application deadline is 12 January 2026. For more details and to apply, visit the University of Exeter funding page and follow the application instructions. This is an excellent opportunity to join a dynamic research team and contribute to innovative solutions for environmental resilience.