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The University of Manchester

FSE PhD President's Doctoral Scholarship: Geospatial Foundation Modelling to Improve Agricultural Monitoring in Smallholder Farming Systems The University of Manchester in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Nov 18, 2026

Country flag

Country

United Kingdom

University

The University of Manchester

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Keywords

Computer Science
Environmental Science
Agriculture
Remote Sensing
Geography
Transfer Learning
Earth Science
Earth Observation
Smallholder Farming

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About this position

[Annual stipend at the UKRI minimum rate currently £21,805 per year plus £1,000 per year enhancement, and a research training support grant of up to £20,000 for the full 4-year programme.]

This 4-year PhD project at The University of Manchester focuses on the use of geospatial foundation models to improve the monitoring of smallholder agricultural systems from satellite data. The work sits at the intersection of Earth Observation, remote sensing, and agricultural mapping, with an emphasis on understanding whether modern foundation-model embeddings can outperform more conventional features and models in challenging small-farm settings.

Smallholder farms are difficult to map because fields are small and irregular, cropping is diverse and mixed, cloud cover is persistent, and labelled reference data are scarce. The project will investigate whether geospatial foundation models such as TESSERA and AlphaEarth can help overcome these limitations by reducing labelling requirements and improving transferability across regions, years, and farming systems.

The student will examine the robustness of current evidence and benchmarking approaches, test when and where geospatial foundation models provide gains, and analyse what agronomic information their embeddings capture. The project is designed to compare frozen and fine-tuned embeddings, single- and multi-modal approaches, and annual versus sub-seasonal use cases, with the broader aim of understanding both the strengths and limitations of these models for smallholder agricultural remote sensing.

The research is supervised by Dr. Timothy Foster and is part of a competitive doctoral scholarship opportunity in the Faculty of Science and Engineering.

Funding details

Available

What's required

Applicants are expected to hold, or be about to obtain, a minimum upper second-class undergraduate honours degree or equivalent in computer science, environmental science, physical geography, environmental engineering, or a related discipline. A Masters-level qualification in a similar or related discipline would be desirable. Skills in programming (Python, R, or equivalent) and data science are essential, and research experience in remote sensing or Earth Observation is desirable.

How to apply

Contact Dr. Timothy Foster to discuss the project before applying. Submit a formal application through the online application portal for the FSE PhD President's Doctoral Scholarship programme. Include the full project title, supervisor details, previous study information, referee details, a supporting statement, CV, and transcripts. Late or incomplete applications will not be considered.

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