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Fully Funded DPhil Studentship in AI, Remote Sensing, and Forest Resilience to Climate at the University of Oxford

Fully Funded DPhil Studentship in AI, Remote Sensing, and Forest Resilience to Climate at the University of Oxford
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The University of Oxford is inviting applications for a fully funded doctoral studentship supported by the Leverhulme Trust, beginning in April 2026. This opportunity will be based at the Environmental Change Institute (ECI) within the School of Geography and the Environment (SoGE), and hosted by the Biodiversity and Earth Observation (BioEO) group.

The Leverhulme Trust Studentship is titled:
AI, Remote Sensing, and Forest Resilience to Climate – Insights for Public Health.

This project brings together advanced technologies and interdisciplinary research to explore how climate-driven stresses affect tropical forests and the public health outcomes linked to them. It provides an exceptional opportunity for researchers to study the intersections of climate change, forest ecology, artificial intelligence, and human health.


Project Overview

The project will focus on the Amazon rainforest, one of the most biodiverse and climate-critical ecosystems in the world. Climate-driven stresses such as drought and heat are reshaping forest functional strategies. The doctoral research will investigate how these ecological shifts intersect with public health outcomes and socio-economic vulnerability across municipalities.

Key aspects of the project include:


Research Objectives

The studentship has four main objectives:

  1. Characterise tree functional strategies using trait datasets and permanent plots, and develop AI/ML models to predict these strategies from UAV and satellite spectra combined with climate, hydrology, and soil data.

  2. Reconstruct multi-decadal shifts in forest functional strategies using harmonised Landsat-Sentinel datasets, while testing how agrarian economic contexts (Techno-productive Trajectories) influence climate-forest relationships.

  3. Model associations between shifts in forest strategies and disease outcomes such as dengue, malaria, leishmaniasis, Chagas, schistosomiasis, leptospirosis, hantavirus, and others.

  4. Evaluate socio-economic vulnerability to identify contexts in which risks between forest change and health outcomes are either amplified or reduced.


Supervisory Team

The doctoral student will be supervised by a team of international experts:

This team provides expertise in ecology, AI, epidemiology, and socio-environmental systems, ensuring an interdisciplinary and comprehensive research environment.


Studentship Details

Training and Development Opportunities


Eligibility Requirements

Applicants should demonstrate:

Desirable Skills and Experience

Applicants from underrepresented backgrounds and communities are especially encouraged to apply.


Application Process

Applications must be submitted via the University of Oxford’s online graduate admissions system: www.ox.ac.uk/admissions/graduate

When applying:

Please note: Word limits for application documents are strictly enforced. Submissions exceeding the limits will be filtered out automatically.

Applicants selected for interview will receive guidance on the process, with interview questions shared in advance.

Deadline for Applications


Conclusion

The Leverhulme Trust Studentship in AI, Remote Sensing, and Forest Resilience to Climate – Insights for Public Health at the University of Oxford is a prestigious, fully funded opportunity for doctoral study. Combining advanced technologies such as artificial intelligence with pressing questions in tropical forest ecology and public health, the project offers a unique platform for impactful, interdisciplinary research.

With full financial support, access to cutting-edge resources, and guidance from a world-class supervisory team, this studentship provides an exceptional pathway for emerging researchers committed to addressing global challenges at the intersection of environment, health, and technology.

For more Information and Applications:

Visit HERE

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