Connectionists: Fully-Funded PhD Studentship - NatureNet: AI systems for conservation and the management of human-wildlife conflict

[Matthias Rolf]

*Key facts*
Start dates: September 2026
Application deadline: *20 February 2026*
Supervisor: Dr Matthias Rolf, Dr Miya Warrington, Dr Shadi Eltanani
Bursary p.a: The stipend is at UKRI rate (currently £20,780 for academic 
year 2025/26)
Fees: University fees and bench fees will be met by the University. Visa 
and associated costs are not funded.

*Overview*
Human-wildlife conflict is widespread, yet current monitoring systems 
aiming at their reduction remain costly, vulnerable, and difficult to 
scale. This project will focus on computational and engineering 
innovation by developing wildlife tracking technologies, including 
integrating advanced AI-based analytics to create a novel prototype for 
conflict mitigation.

The work will involve developing a data processing pipeline capable of 
handling complex behavioural datasets from biologgers and autonomous 
monitoring units, enabling accurate movement and interaction analysis 
without reliance on GPS-based technology. In the final phase, the 
prototype will be tested in real-world conditions such as involving 
predators and/or semi-domesticated livestock, in collaboration with 
local and Indigenous stakeholders (Europe, the Global South).

This project develops novel computational methods to meet conservation 
needs, delivering a transformative solution for reducing human-wildlife 
conflict.

*Additional details*
This project would suit individuals with an interest in technology 
development and computational methods to be applied towards wildlife 
monitoring and conservation.

The student will join two complementary research environments: The 
C-Wild Warrington lab (Ecology and Conservation) and the Machine 
Learning and Robotics research group (Artificial Intelligence, Data 
Analysis and Systems). This will provide interdisciplinary training, 
enabling the student to integrate ecological knowledge with advanced 
computational methods for developing and testing innovative wildlife 
monitoring solutions.

The student should have strong computational skills, including 
programming (Python or similar), data analysis, and familiarity with 
machine learning for time-series and sensor data. Basic knowledge of 
designing and building electronic units (e.g., biologgers, IoT systems), 
experience managing large datasets, and prior exposure to working in 
wild or remote field settings are essential.

*More Information*
https://www.brookes.ac.uk/courses/research/nigel-groome-studentship-naturenet-ai-systems-for


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Dr. Matthias Rolf                  Machine Learning and Robotics Group
Associate Professor in Computer Science
First Year Tutor & Postgraduate Research Tutor for Computing
Impact Champion for Engineering and Computing
Oxford Brookes University
http://www.brookes.ac.uk/ecm/                          +44 1865 603172
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