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Oisin Mac Aodha

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PhD in AI-Based Identification of Emerging Zoonotic Disease Hotspots University of Edinburgh in United Kingdom

I am offering a fully funded PhD position in AI-based identification of emerging zoonotic disease hotspots at the University of Edinburgh.

University of Edinburgh

United Kingdom

email-of-the@publisher.com

Jan 20, 2026

Keywords

Computer Science
Epidemiology
Public Health
Environmental Science
Biology
Remote Sensing
Artificial Intelligence
Biodiversity
Biosecurity
Medical Science
Land Management
Spatial Modelling
Avian Influenza
Machinelearning
Zoonotic Diseases
Species Distribution Modelling

Description

This fully funded PhD project at the University of Edinburgh's School of Informatics focuses on developing advanced AI-based solutions to identify and mitigate the spread of emerging zoonotic and infectious diseases. The research is motivated by the urgent need to address threats to human health posed by diseases such as Highly Pathogenic Avian Influenza, which are increasingly prevalent due to climate change, habitat loss, and intensified human-animal interactions. The project aims to create new computational tools for predicting disease hotspots by integrating diverse data sources, including human population data, remote sensing, and species observation records. By leveraging recent advances in multi-modal artificial intelligence and spatial modelling, the student will develop techniques to estimate global biodiversity at scales relevant to pathogen circulation and landscape management. The research will also involve identifying likely zoonotic disease hotspots using existing infection datasets and recommending land management strategies to enhance resilience against disease spread. Supervision will be provided by Dr Oisin Mac Aodha and Professor Rowland Kao, with additional mentorship and real-world data access from the Animal and Plant Health Agency (APHA), a key UK government partner. The student will be integrated into the supervisors' research groups and the broader Edinburgh Infectious Diseases network, benefiting from regular group meetings, collaborative opportunities, and tailored training to address any knowledge gaps. The project outputs will include open-access models and data products for spatial risk prioritisation and species distribution, supporting practitioners and researchers in public health and ecological fields. The work aligns with the UK Biological Security Strategy and will contribute to pandemic preparedness and biosecurity efforts. The studentship is part of the UKRI AI Centre for Doctoral Training in Biomedical Innovation and offers a comprehensive funding package: full tuition fees, a stipend of £20,780 (2025/26), and an individual budget for travel and research costs. Additional allowances for sick pay and maternity leave are included, and eligibility is open regardless of nationality or domicile. Applicants should have a strong academic background in a relevant discipline (such as computer science, biology, or mathematics), experience or interest in AI and machine learning, and good programming skills. English language proficiency must meet university standards. The application deadline is January 20, 2026. For more information and to apply, visit the project page or the University of Edinburgh's application portal. Early contact with the supervisors is encouraged for specific queries.

Funding

Funded PhD Project (Students Worldwide)

How to apply

Apply through the University of Edinburgh's application portal for the UKRI AI CDT in Biomedical Innovation. Prepare your academic transcripts, CV, and a statement of research interests. Contact the supervisors if you have specific questions about the project. Ensure your application is submitted before the deadline.

Requirements

Applicants should hold, or expect to obtain, a first-class or upper second-class degree (or international equivalent) in a relevant discipline such as computer science, biology, mathematics, or a related field. Experience or strong interest in artificial intelligence, machine learning, or computational modelling is highly desirable. Good programming skills and a demonstrated ability to work with data are important. English language proficiency must meet University of Edinburgh requirements. No specific GPA or test scores are mentioned, but strong academic performance and motivation are expected.

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