About this role
Funded PhD Studentship in applied geospatial ecology. This project will investigate how dryland vegetation productivity varies across space and time and how it responds to land management. Working with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large-scale regreening efforts.
The research will advance the Relative Productivity Index framework and assess both passive restoration (e.g., reducing grazing pressure) and active interventions such as planting and irrigation. The work will inform restoration decision-making, connect with wider research in the TESS Lab, and includes the option of an extended visit to Saudi Arabia for field learning and collaboration with project partners.