ADVERTISED PROJECTS
 

Climate change and drug resistance are posing real issues for farmers. This project will augment existing systems for monitoring small ruminant health on smallholder farms in Africa by applying machine learning to data sets, integrating real-time health information with predictive models of parasite transmission, and developing a smartphone app.

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.

Systemic fungal infections caused by Candida yeast species are a growing public health concern, with high mortality rates and rising resistance to conventional antifungals. This project will sequence and experimentally characterise over 400 Candida isolates, using AI to identify resistance signatures and predict susceptibility to novel “resistance breaker” compounds.

Intensive livestock farming is a major contributor to environmental degradation, including nutrient runoff, water pollution, and greenhouse gas emissions.The research will involve the development of predictive analytics using federated learning and trustworthy AI techniques, building on the X10AI AGRISMART digital twin platform.

The agri-food supply chain is important to global food security, yet it faces growing challenges in balancing sustainability, fairness, and resilience. The student will model agri-food supply chains using game-theoretic and probabilistic approaches from CS, incorporating factors like logistics, resource allocation, greenhouse gas emissions, and ethical considerations.

Agriculture is undergoing a digital transformation, driven by advances in Artificial Intelligence, Earth Observation and autonomous decision support technologies. This project will develop AI methods that combine radar, optical and hyperspectral data to enable continuous crop monitoring and support crop management decisions.

Agriculture in Ethiopia faces major challenges from declining soil fertility, land degradation, climate variability and reduced crop productivity. Agriculture in Ethiopia faces major challenges from declining soil fertility, land degradation, climate variability and reduced crop productivity. Smallholder farmers often lack access to timely information to support decisions on fertiliser use, water management and sustainable farming practices. Growing interest in regenerative agriculture and carbon sequestration is creating opportunities to improve food security and climate resilience. ETHIO-SENSE will address these challenges by adapting low-cost soil sensing and Monitoring, Reporting and Verification technologies currently being developed in Ghana.

Carbon farming has significant potential to support climate change mitigation while improving the resilience and livelihoods of smallholder farmers. This project will combine field observations, remote sensing and AI to estimate carbon outcomes and determine how to achieve reliable monitoring at minimum cost.

Sugar beet is a strategically important crop whose productivity is increasingly threatened by insect pests. This PhD will use extensive historical datasets and field sites provided by the British Beet Research Organisation (BBRO) to develop and test next-generation monitoring frameworks.