PhD Studentship: Integrating Machine Learning with One Health Frameworks to Quantify Disease Transmission Pathways between Wildlife and the Environment

Overview

This doctoral studentship focuses on an engineering challenge involving the co-design of sensing hardware and intelligent data processing to capture biologically meaningful information from wild animals. The research integrates machine learning with frameworks to quantify disease transmission pathways and address limitations in existing field technologies, which are frequently constrained by device mass, size, power, and environmental robustness.

The project brings together supervision across behavioural field biology, passive monitoring, ecological applications of artificial intelligence, and electronic engineering. The student will conduct testing and validation using established wild-animal study populations, including the Cornish Jackdaw Project in the United Kingdom and field sites in West Africa.

Who can apply

The position is suited for an interdisciplinary candidate with an interest in applying computer science, engineering, or artificial intelligence to real-world biological problems.

What you get

The studentship covers tuition fees and provides a stipend of £21,805 per annum for the 2026/7 academic year. The successful candidate will gain transferable skills in data science, engineering, and ecological research, as well as opportunities for stakeholder engagement, technology translation, and commercialisation.

Key dates

The closing date for applications is 8 December 2026.