PhD Studentship - AI-Driven Remote Sensing for Species-Level Savannah Monitoring

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This PhD will pioneer the first species-level monitoring framework using drone-based multispectral data fused with very-high-resolution satellite imagery (Pleiades Neo), powered by cutting-edge geospatial AI.

You will develop a scalable training pipeline to map species-level encroachment across landscapes, combining drone data with satellite products (Sentinel1/2, EnMAP, GEDI). The project is co-designed with South African government agencies and supported by Airbus, providing premium satellite imagery and technical expertise. While the project includes methodological and applied components, the primary focus will be on developing and validating the scalable geospatial AI framework, with field and policy integration supported through established collaborations.

You’ll gain advanced skills in remote sensing, AI, ecological modelling, and policy engagement, working across disciplines and continents. The project includes an industrial supervisor to support non-academic training and skills development. You’ll contribute to open-source tools and decision-ready indicators for restoration and land management impacting savannahs.

Project aims and objectives

Main Aim: To develop a scalable, species-level monitoring framework for woody vegetation encroachment in African savannahs using drone and satellite data fused with advanced geospatial AI.

Specific Objectives:

• Design and implement a hierarchical training pipeline linking UAV multispectral data with very-high-resolution satellite imagery (Pleiades Neo).
• Conduct field campaigns in South Africa using a multispectral UAS
• Apply self-supervised and interpretable deep learning models to upscale species-level mapping to regional satellite products.
• Organise co-creation workshops with local stakeholders and generate decision-ready indicators for restoration and land management, co-designed with South African government agencies.
• Develop open-source tools (QGIS plugin and web viewer) to support operational uptake and policy integration.

Funding

Both Home and International students can apply. Only home tuition fees will be covered for the duration of the 3.5-year award, which is £5,006 for the year 2025/26 (applied pro-rata for part-time study, if applicable). Eligible international students will need to make up the difference in tuition fee funding (Band 3 for the year 2025/26).

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26 (applied pro-rata for part-time study, if applicable).

Specific requirements of the candidate

Essential requirements:

• A 1^st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field
• Strong coding skills (Python preferred)
• Experience with processing and analysing remotely sensed data
• Experience with GIS and spatial data analytical techniques
• Experience with carrying out fieldwork in related fields (e.g. Geography, Environmental Science, Ecology)

How to apply

Interested applicants should contact Elias Symeonakis (e.symeonakis@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full time or part-time PhD in Life Sciences.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.

Please upload these documents in the supporting documents section of the University’s Admissions Portal.

Applications closing date: 28^th February 2026.

Expected start date: 1^st October 2026.

Please quote the reference: SciEng-ES-2026-27-Savanna AI Monitoring

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