Meet ApplyKite Twin, the application assistant that learns with you
Meet your Twin, it learns with you
Your Twin learns with you
Launch week codes:30% off annual Pro20% off monthly Pro
Ends in00h00m00s
Ends in00h00m00s
University of Exeter
Just Landed
New Today
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
United Kingdom
University
University of Exeter
Apply for this scholarship
Keywords
Suggested scholarships
About this position
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.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the provider.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the provider.