Queen's University Belfast
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Fully Funded PhD in AI for One Health Decision Support Under Climate Change Queen’s University Belfast in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded four-year PhD studentship. Tuition fees are paid. Includes a tax-free UKRI stipend of £21,805 per year (2026/7 rate) for living costs, a Research Training Support Grant of £3,000 per year (up to £12,000 total) for travel, training and consumables, plus additional funding for outreach, dissemination, summer schools, research events, and development projects.
Deadline
Oct 16, 2026
Country
United Kingdom
University
Queen's University Belfast

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About this position
Fully funded PhD project at Queen’s University Belfast within the SUSTAIN CDT on One Health, Artificial Intelligence, Machine Learning, Data Science, Veterinary Medicine, and Agricultural Sciences.
The project, Combining Hazard Prediction and Animal and Plant Health Diagnostics for Enhanced One-Health Decision Support Under Climate Change, focuses on climate change, drug resistance, parasite transmission, and livestock health in smallholder farming systems, especially in Africa. The research will integrate real-time animal health information with predictive models, use machine learning to identify useful health indicators and monitoring strategies, and develop climate-driven risk predictions. A smartphone app will be co-produced with farmers and advisors to support decision-making on antiparasitic interventions, and the project will also explore links to plant health risk tools for broader food-security support.
The appointed student will receive training in machine learning, app development, animal health, and epidemiology, and will work closely with stakeholders in Africa and other partners to co-develop and field-test the app. This is a strong fit for applicants interested in interdisciplinary digital health, sustainable agri-food systems, and applied AI for real-world impact.
Funding includes full PhD tuition, a tax-free UKRI stipend of £21,805 per year (2026/7 rate), and a Research Training Support Grant of £3,000 per year, plus additional support for outreach, dissemination, summer schools, research events, and development projects.
Applicants should have at least a 2:1 honours degree in Statistics, Data Science, Animal Science, or Veterinary Science with a quantitative focus, plus skills or experience in programming, modelling, data science, machine learning, or AI. A Master’s degree is advantageous. The deadline is 16 October 2026 at 12:00 noon BST.
Apply via the SUSTAIN CDT website: https://www.sustain-cdt.ai/how-to-apply. Check eligibility before applying and contact Professor Eric Morgan for enquiries.
Funding details
Fully funded four-year PhD studentship. Tuition fees are paid. Includes a tax-free UKRI stipend of £21,805 per year (2026/7 rate) for living costs, a Research Training Support Grant of £3,000 per year (up to £12,000 total) for travel, training and consumables, plus additional funding for outreach, dissemination, summer schools, research events, and development projects.
What's required
Honours degree minimum 2:1 in Statistics, Data Science, Animal Science or Veterinary Science with a quantitative focus, including programming, modelling, data science, machine learning, or AI. A Master's degree is an advantage. Applicants should be self-driven, curious, interested in interdisciplinary work, eager to work in a team, and willing to engage in departmental events, seminars, and public/policy engagement. Desirable: knowledge of agricultural or livestock systems, environmental sciences, veterinary science, a Master's in AI, Machine Learning, Environmental Sciences, Biological or Agricultural Science or similar, and a quantitatively focused dissertation or thesis.
How to apply
Visit the SUSTAIN CDT application page for full instructions and submit the application through the programme website. Check eligibility on the SUSTAIN website before applying. Direct enquiries can be sent to Professor Eric Morgan.
More information can be found here
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