Optimising Sustainability in Livestock Systems: A Multi-Criteria Assessment Framework

About the project

About the Project Shape the Future of Sustainable Livestock Systems We are seeking a skilled, ambitious, and forward‑thinking researcher to join a groundbreaking project developing methodologies for the holistic assessment of livestock systems . If you are driven to apply advanced AI to real‑world challenges, this studentship offers the perfect platform to make a meaningful impact. Agricultural sustainability is inherently multidimensional, spanning environmental, economic, and social domains. Each domain contains multiple impact metrics — from carbon footprint and ammonia emissions to welfare outcomes and antimicrobial use. These metrics often conflict, meaning no single management strategy excels across all dimensions . This is where sustainability multicriteria optimisation becomes transformative. It provides a structured, rigorous way to evaluate livestock systems or indeed agricultural systems when multiple objectives must be balanced simultaneously, revealing the most feasible and well‑balanced solutions. Your Mission You will develop a methodology for optimising multiple sustainability objectives within livestock systems. A system will be described by management decisions — feeding strategies, stocking density, genotype, health interventions — each influencing sustainability outcomes. Rather than collapsing these outcomes into a single score, you will treat each as a separate objective, positioning them in a multi‑dimensional optimisation space . Your work will: Evaluate normalisation needs across sustainability dimensions using real livestock datasets. Select and implement optimisation approaches — from Pareto‑optimality to advanced methods such as genetic algorithms. Develop a hybrid AI framework combining machine learning with computational argumentation to both optimise and explain outcomes. Engage stakeholders to assess the clarity, usefulness, and real‑world applicability of the model. Why This Matters Your work will help farmers, policymakers, and industry leaders understand not just what decisions lead to sustainable outcomes, but why — empowering transparent, explainable, and future‑ready livestock management. 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. Our fully-funded package includes: All PhD tuition fees paid A tax-free UKRI stipend of £21,805 (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, or Veterinary Science, with a qualitative focus that included, 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 PhD 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, show enthusisasm for public / policy engagement activities Desirable: A Master’s degree in AI, Machine Learning, Environmental Sciences, Biological or Agricultural Science or similar, is highly desirable. Quantitative or technically focused Dissertation / Thesis. Enquiries: Professor Ilias Kyriazakis i.kyriazakis@qub.ac.uk Application Deadline: 12:00 noon (BST) on Friday, 16 October 2026

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