Combining Hazard Prediction and Animal and Plant Health Diagnostics for Enhanced One-Health Decision Support Under Climate Change

About the project

About the Project Climate change and drug resistance are posing real issues for farmers, threatening productivity and the sustainability of livestock farming. Problems are especially acute in areas such as Africa, where disease burden is highest and options for control most limited. The project will augment existing systems for monitoring small ruminant health on smallholder farms in Africa by integrating real-time health information with predictive models of parasite transmission – and delivering actionable advice on antiparasitic interventions. The project will draw on existing and new datasets, applying machine learning to identify the most informative health indicators and the most efficient and effective monitoring strategies. Then, climate-driven predictions of parasite transmission potential will be added so that monitoring and action can be calibrated to epidemiological risks. The key output will be a smartphone app to provide this capability to farmers and advisors, with whom the app will be co-produced. Finally, the project will explore the potential to align the app with comparable risk prediction tools for plant health, supporting farmers and others to identify and head of multiple threats to food security through simultaneous impacts on crops and animals. The appointed student will benefit from training in machine learning, app development, animal health and epidemiology. They will work closely with stakeholders, including in Africa, to co-develop and deliver the app and apply it in the field, learning from user experiences to optimize design and delivery. They will emerge with cutting-edge skills and experiences in digital health that are in strong demand in research, NGO, public and private sectors. Interested applicants should visit ' https://www.sustain-cdt.ai/how-to-apply ' for full instructions on how to submit their application. Supervisors: Professor Eric Morgan ( eric.morgan@qub.ac.uk ) Professor Adam Kleczkowski ( a.kleczkowski@strath.ac.uk ) Paul Wagstaff, Self-Help Africa Rob Strey, PEAT GmbH / Plantix Applications: Applications are invited for fully-funded four-year PhD studentships to join the SUSTAIN doctoral training programme, undertaking research in the application of Artificial Intelligence to sustainable agri-food. Applications for our October 2027 cohort are now open. Please visit the SUSTAIN website to check your eligibility before applying. Our fully-funded studentship package includes: All PhD tuition fees paid A tax-free UKRI stipend of £21,805 per year (2026/7 rate) to cover living costs A Research Training Support Grant (RTSG) of £3,000 each year to support travel, training and consumables costs (up to £12,000 in total) Additional funding to support outreach and dissemination, attendance at summer schools, research events, and development projects Requirements: Honours degree (minimum 2:1) in Statistics, Data Science, Animal Science of Veterinary Science with a quantitative focus that includes, for example, programming, modelling, data science, machine learning and AI. A Master's degree is an advantage. Most importantly, you are motivated to apply advanced AI and analytical tools to enhance the sustainability of food production systems – and to create solutions that genuinely matter. It is essential that the student is self-driven, curious, interested in working across disciplines and exploring new areas, as well as eager to work as part of an interdisciplinary team. The student will be expected to engage with their peers and other academic staff, get involved in departmental events and seminars, and show enthusiasm for public / policy engagement activities. Desirable: A knowledge of agricultural or livestock systems, environmental sciences, veterinary science A Master’s degree in AI, Machine Learning, Environmental Sciences, Biological or Agricultural Science or similar. Quantitative or technically focused Dissertation / Thesis Enquiries: Professor Eric Morgan ( eric.morgan@qub.ac.uk ) Application Deadline: 12:00 noon (BST) on Friday, 16 October 2026

Research areas