Research Studentship in Wildlife Monitoring, University of Oxford

3.5-year DPhil studentship 

Supervisors: Professor Maurice Fallon

Lyme disease is a bacterial illness that can cause fever, fatigue, joint pain, and skin rash, as well as more serious joint and nervous system complications. Lyme disease is the most common vector-borne disease in the United States. It is estimated that over 89,000 cases of Lyme disease were reported to the Centre for Disease Control and Prevention by US state health departments and the District of Columbia in 2023 - and this number is growing year-on-year. The locations and periods of the year where the disease is prevelant is also changing - due to climate change.

The disease is spread by ticks who are present in forests and woodlands - making it dangerous for people to enjoy their local wildlife parks and forest. Our vision is to develop remotely deployable monitoring stations to track the presence and activity of ticks through mice and other rodents – who are common hosts for these arachnids.

Our goal is to study and develop technology and cyber physical systems (i.e traps) to enable data-driven analysis of where ticks are presence. The project will involve image processing for traditional cameras and thermal cameras to deployed “on the edge” as well as the development of classification and statistical activity analysis.

We will work closely with our donor to deploy the prototype technology in a real-world testbed (likely to be the United States) with the goal of deploying a sensor network enabling spatial modelling of the activity of the parasite across a large area. Key scientific questions we are interested in answering are where and when are the ticks active – and how confident can we be of their detection. We will also explore the adaption of this technology for other monitoring and behaviour analysis tasks.

This studentship is funded by a generous donation from the Breitman Family Foundation.

Eligibility

This studentship is funded through the Department of Engineering Science and is open to home and overseas students.

Award Value

Course fees are covered at home level. The stipend (tax-free maintenance grant) is provided at c. £20,780 p.a. for the first year, and at least this amount for a further 2.5 years. Overseas students are eligible for this studentship but would need to self-fund the difference in home and overseas fees.

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

•  A first class or strong upper second-class undergraduate degree with honours (or equivalent) in Engineering, Computer Science, Biology or Physics
• Excellent English written and spoken communication skills

The following skills are desirable but not essential:

• Previous study in computer vision, robotics, electronics or related disiplines
• An interest in biodiversity monitoring and public health
• Ability to program in Python, C++ or related languages

Application Procedure

Informal enquiries are encouraged and should be addressed in the first instance to Professor Maurice Fallon (mfallon@robots.ox.ac.uk).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.

Please quote 26ENGIN_MF in all correspondence and in your graduate application.

Application deadline: noon on 3 March 2026 (In line with the University admissions deadline set by the University)

Start date: October 2026