About the Project
Intensive livestock farming is a major contributor to environmental degradation, including nutrient runoff, water pollution, and greenhouse gas emissions. Existing regulatory frameworks often rely on fixed calendar-based rules and retrospective measurements, which fail to reflect the dynamic nature of farm systems. This PhD project addresses the urgent need for data-driven, site-specific approaches to nutrient and carbon management that support both environmental sustainability and agricultural…
productivity.
The research will involve the development of predictive analytics using federated learning and trustworthy AI techniques. It will build on the X10AI AGRISMART digital twin platform, which integrates real-world data from anaerobic digestion, ammonia recovery, pyrolysis, and precision agriculture across UK farms. The project will focus on fusing hyperspectral drone imagery with structured (e.g., yield, weather) and unstructured (e.g., farm logs, regulatory reports) datasets through multimodal data harmonisation and summarisation using large language models.
A federated learning framework will be designed to enable collaborative model training across farms while preserving data privacy. The models will incorporate both physics-informed and data-driven components and will be validated through two case studies: (1) predicting grass growth to support phosphorus “geo-mining” and sustainable manure export, and (2) forecasting slurry spreading windows based on local soil and weather conditions.
Training will include advanced skills in machine learning, remote sensing, environmental modelling, and explainable AI. The PhD offers opportunities to work with academic experts and industry partners (x10AI, ABP, Sainsbury’s, NFU), access real-world datasets, and contribute to research with direct environmental protection policy and industry relevance.
The PhD project is ideal for students with strong programming and mathematical skills, and a passion for AI and sustainability.
Supervisors:
- Prof. Sean McLoone (s.mcloone@qub.ac.uk)
- Dr. Iain Gould (igould@lincoln.ac.uk)
- Dr. Shaun Coutts (scoutts@lincoln.ac.uk)
- Mr Thomas Cromie, X10AI
Applications:
Our fully-funded studentship package includes:
- All PhD tuition fees paid.
- A tax-free stipend at UKRI rates 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.
Interested applicants should visit https://www.sustain-cdt.ai/how-to-apply for full instructions on how to submit their application.
Requirements:
Candidates must have an honours degree (minimum 2:1) in Computer Science, Engineering or related disciplines, a strong mathematical background and good programming skills.
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, and show enthusiasm for public/policy engagement activities.
Desirable:
- a Master’s degree in AI, Machine Learning, Data Science, or Control Systems
- Experience of working with AI for practical applications
- Prior experience of engaging with industry on R&D projects
Enquiries:
SUSTAIN@Lincoln.ac.uk
Application Deadline:
12:00 Midday on Friday, 16th October 2026 (UK time)
References
- D. Cordell et al., UK Phosphorus Transformation Strategy: towards a circular UK food system, RePhoKUS Report, 2022, https://zenodo.org/records/7404622.
- J. Zhou, Y. Guo, Z. Yang. J. Yang, Z. An, K. Li and S. McLoone, T2MFDF: A LLM-Enhanced Multimodal Fault Diagnosis Framework Integrating Time-series and Textual Data, IEEE Transactions on Instrumentation and Measurement, June 2025.
- M.V. Luzón, et al., A tutorial on federated learning from theory to practice: Foundations, software frameworks, exemplary use cases, and selected trends, IEEE/CAA Journal of Automatica Sinica, Vol. 11 (4), pp. 824-850, 2024.
- S. Oladele, I. Gould, and S. Varga. Low-carbon footprint organo-mineral fertilizer increases potato yield, N uptake, and soil nutrient levels comparable to conventional fertilizer, Potato Research, April 2025.
- R.M. Goodsell et al., Black-grass monitoring using hyperspectral image data is limited by between-site variability, Remote Sensing, Vol. 16 (24), 2024.