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University of Exeter

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PhD Studentship: Can we trust AI to help save biodiversity? University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Jan 7, 2027

Country flag

Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Environmental Science
Biology
Remote Sensing
Artificial Intelligence
Computer Vision
Uncertainty Analysis
Aeroacoustics
Ecological Modeling
Statistics
Statistical Modelling

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About this position

[Home tuition fees plus an annual tax-free stipend for eligible students.]

This PhD studentship is based at the University of Exeter and explores whether AI can be trusted to support biodiversity conservation decisions. The project asks when automated monitoring produces reliable ecological evidence, and when errors in machine detection may distort conclusions about species presence, abundance and population trends.

The research brings together large biodiversity datasets, simulation and statistical modelling to study how false positives, false negatives and uncertainty propagate through ecological analyses. The student will investigate how species traits, taxonomy, habitat, season, environmental conditions and the availability of training data shape AI performance, and will assess which ecological contexts are most challenging for automated monitoring.

A major theme of the project is improving trust in automated monitoring workflows. The student will examine approaches such as using ecological context to refine uncertainty estimates and combining multiple sensing modalities, including cameras, acoustics, eDNA and remote sensing, to strengthen inference. The project is designed to support both methodological development and practical conservation use, with an emphasis on turning AI outputs into evidence that can be applied in real-world decision-making.

The studentship sits within an interdisciplinary collaboration between ecology and computer science. Benno Simmons, Sareh Rowlands and Chris Clements will provide hands-on supervision and training through coding, joint analysis, research meetings and paper writing. The student will also gain experience communicating technical findings to conservation organisations, local environmental partnerships and policy audiences, and will have the opportunity to develop their own ideas, choose focal taxa and datasets, and shape the direction of the research.

The wider training environment includes reproducible data science, AI and computer vision, hierarchical ecological modelling, uncertainty quantification, high-performance computing, scientific writing and communication. The Exeter–Bristol collaboration gives access to complementary expertise and research communities, including research visits to Bristol.

Funding details

Available

What's required

No specific entry requirements are stated in the advert. The project is suitable for a student with interest in biodiversity monitoring, AI/computer vision, ecological modelling, data science and quantitative analysis; training will be provided in reproducible data science, hierarchical ecological modelling, uncertainty quantification, high-performance computing, scientific writing and communication.

How to apply

Apply via the Exeter funding application page using the provided application link. Review the project details and submit your application before the deadline. If required, follow any instructions on the funding page for supporting documents.

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