About the Project
Systemic fungal infections caused by Candida yeast species are a growing public health concern, with high mortality rates and rising resistance to conventional antifungals. Emerging evidence indicates that environmental exposure to agricultural fungicides contributes to resistance in clinical strains, creating a critical link between crop protection practices and human health. Addressing this challenge requires a systems-level understanding of how agricultural practices shape fungal evolution…
and therapeutic outcomes.
This project aims to leverage artificial intelligence (AI) and genomics to transform our understanding of these complex interactions and inform more sustainable agricultural and clinical strategies. We will generate a comprehensive dataset comprising over 400 Candida isolates sourced from both clinical infections and agricultural environments in Northern Ireland. Each isolate will be fully sequenced, and its resistance profile to conventional antifungals and novel “resistance breaker” compounds will be experimentally characterised.
Using deep learning, we will model high-dimensional genomic data to: (1) identify genetic signatures associated with fungicide-driven resistance, (2) predict strain-specific susceptibility to resistance breakers, and (3) uncover biomarkers capable of guiding both clinical therapy and agricultural interventions. AI-driven predictions will be validated through cross-validation and held-out datasets to ensure robustness and reproducibility.
The project will generate multiple tangible outputs, including a curated genomic dataset, predictive AI models, validated biomarkers, and evidence-based recommendations for sustainable fungicide use. Risk maps highlighting environmental hotspots for resistance emergence will inform crop management policies, while engagement with farmers and stakeholders will support practical implementation.
By integrating genomics, AI, and a One Health perspective, this project provides a blueprint for sustainable agri-food systems where crop protection practices are optimised without compromising public health. It exemplifies how AI can drive innovation in sustainable food systems, linking agricultural practices, environmental stewardship, and human health in a single, actionable framework.
Supervisors:
- Dr Edel Hyland
- Dr Yining Hua
- Dr Lorena Rangel
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:
Honours degree (minimum 2:1) in [Microbiology, Genetics/Genomics, Biochemistry, Biology ] with strong expertise in [bioinformatics and/or antimicrobial drug resistance]
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,
- Quantitative or technically focused BSc/MSc Dissertation/Thesis.
Enquiries:
SUSTAIN@lincoln.ac.uk
Application Deadline:
Friday, 16th October 12:00 midday (UK time)
References
- Allison J, and Hyland EM, “From Crops to Clinic: The Impact of Dual Azole Use on Antifungal Resistance in Candida and Candida Associated Yeasts”, Front. Micro. Biol. In press, September 2025
- Williams CC, Gregory JB, Usher J. Understanding the clinical and environmental drivers of antifungal resistance in the One Health context. Microbiology (Reading). 2024 Oct;170(10):001512.
- Thorn V, Xu J. From patterns to prediction: machine learning and antifungal resistance biomarker discovery. Can J Microbiol. 2025 Jan 1;71:1-13.
- Fu C, Zhang X, Veri AO, Iyer KR, Lash E, Xue A, Yan H, Revie NM, Wong C, Lin ZY, Polvi EJ, Liston SD, VanderSluis B, Hou J, Yashiroda Y, Gingras AC, Boone C, O'Meara TR, O'Meara MJ, Noble S, Robbins N, Myers CL, Cowen LE. Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets. Nat Commun. 2021 Nov 11;12(1):6497.