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Gary Watmough

Top university

11 months ago

PhD in Interpretable Poverty Estimation Using AI and Earth Observation at University of Edinburgh University of Edinburgh in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Edinburgh

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Keywords

Computer Science
Data Science
Environmental Science
Geography
Artificial Intelligence
Public Policy
Earth Observation
Spatial Statistics
Poverty Alleviation
Immunization
Interdisciplinary Collaboration
Pediatric Nutrition
Statistic
Geospatial Information

About this position

The University of Edinburgh is offering a PhD opportunity focused on developing interpretable and local poverty estimations using advanced AI and earth observation technologies. The project aims to create actionable poverty maps for policymakers by leveraging satellite data and machine learning, with a strong emphasis on interpretability and real-world impact. The research will involve assessing current AI and ML tools for poverty estimation, designing AI-driven processes for policy applications, and developing prototype models to support decision-making in areas such as child nutrition and vaccination.

The successful candidate will join an international, interdisciplinary team and benefit from comprehensive training in earth observation, spatial statistics, and programming (Python/R), as well as professional skills development. Applicants from diverse academic backgrounds, including geography, geosciences, computer/data sciences, ecology, and social sciences, are encouraged to apply. The position is based at the University of Edinburgh, with collaboration between the Schools of Geosciences and Informatics. The application deadline is December 14, 2025.

For more information and application instructions, refer to the link in the comments.

Funding details

Available

What's required

Applicants should have a background in geography, geosciences, computer or data sciences, ecology, or social sciences. A willingness to learn and collaborate across disciplines is highly valued. No specific technical skills are required, but experience with Python or R, spatial statistics, or earth observation is beneficial. Candidates should be motivated to address global challenges and work in an interdisciplinary team.

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Professors

Programme Director

University Name
Country flag

United Kingdom

Research Interests

Human Geography

Cartography

Geospatial Information

Earth Observation

Poverty Alleviation