PhD Studentship – AI-Driven Remote Sensing for Species-Level Savannah Monitoring Manchester Metropolitan University in United Kingdom

[Home tuition fees (£5,006/year for 2025/26) covered for 3.5 years; international students must pay the difference. Standard UKRI stipend (£20,780/year for 2025/26) for the duration of the award. Pro-rata for part-time study.]

This PhD studentship at Manchester Metropolitan University offers a unique opportunity to pioneer species-level monitoring of woody vegetation encroachment in African savannahs using advanced geospatial AI. The project integrates drone-based multispectral data with very-high-resolution satellite imagery (Pleiades Neo), leveraging cutting-edge AI techniques to develop a scalable training pipeline for mapping species-level changes across landscapes. You will work with satellite products such as Sentinel1/2, EnMAP, and GEDI, and benefit from collaborations with South African government agencies and Airbus, which provide premium satellite imagery and technical expertise.

The research aims to design and implement a hierarchical training pipeline linking UAV multispectral data with satellite imagery, conduct field campaigns in South Africa, and apply self-supervised and interpretable deep learning models to upscale mapping to regional satellite products. The project also involves organizing co-creation workshops with local stakeholders and generating decision-ready indicators for restoration and land management, co-designed with government agencies. You will contribute to the development of open-source tools, including a QGIS plugin and web viewer, to support operational uptake and policy integration.

As a student, you will gain advanced skills in remote sensing, AI, ecological modelling, GIS, and policy engagement, working across disciplines and continents. The project includes an industrial supervisor to support non-academic training and skills development. The studentship is open to both Home and International applicants. Home tuition fees (£5,006/year for 2025/26) are covered for the 3.5-year award, while international students must pay the difference in tuition fees. A standard UKRI stipend (£20,780/year for 2025/26) is provided for the duration of the award, with pro-rata adjustments for part-time study.

Applicants must hold a 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or a related field. Essential skills include strong coding abilities (Python preferred), experience with processing and analysing remotely sensed data, GIS and spatial data analytical techniques, and fieldwork in related disciplines such as Geography, Environmental Science, or Ecology.

To apply, contact Dr Elias Symeonakis for an informal discussion. Complete the online application form for a PhD in Physical Sciences, submit the Doctoral Project Applicant Form, CV, and covering letter via the University’s Admissions Portal, and quote reference SciEng-ES-2026-27-Savanna AI Monitoring. The application deadline is 9 March 2026, with an expected start date of 1 October 2026.