Queen's University Belfast
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PhD in AI and Genomics for Candida Antifungal Resistance Prediction Queen’s University Belfast in United Kingdom
Degree Level
PhD
Field of study
Biochemistry
Funding
Fully funded PhD studentship. All PhD tuition fees are paid. A tax-free stipend is provided at UKRI rates to cover living costs. Includes a Research Training Support Grant of £3,000 per year (up to £12,000 total) for travel, training, and consumables, plus additional funding for outreach, dissemination, summer schools, research events, and development projects.
Deadline
Oct 16, 2026
Country
United Kingdom
University
Queen's University Belfast

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About this position
PhD project at Queen’s University Belfast within the SUSTAIN CDT: Leveraging AI to Predict Strain-Specific Susceptibility of Candida pathogenic yeast to Novel Antifungal “Resistance Breakers”.
This project sits at the intersection of artificial intelligence, genomics, bioinformatics, microbiology, genetics, molecular biology, and antifungal resistance. It addresses the public health challenge of systemic fungal infections caused by Candida species and explores how agricultural fungicide exposure may contribute to resistance in clinical strains. The work uses a One Health perspective linking agriculture, environment, and human health.
The student will work with a dataset of 400+ Candida isolates from clinical and agricultural environments in Northern Ireland, generate whole-genome sequencing data, experimentally characterise resistance profiles, and build deep learning models to identify genetic signatures and predict susceptibility to novel resistance-breaker compounds. Outputs include curated genomic datasets, predictive AI models, biomarkers, and recommendations for sustainable fungicide use.
Funding: fully funded studentship with tuition fees paid, UKRI-rate tax-free stipend, and a Research Training Support Grant of £3,000 per year (up to £12,000 total), plus extra support for outreach and dissemination.
Eligibility: minimum 2:1 honours degree in Microbiology, Genetics/Genomics, Biochemistry, or Biology, with strong bioinformatics and/or antimicrobial drug resistance experience. A master’s in AI/Machine Learning or a quantitative/technical dissertation is desirable.
Deadline: Friday 16 October 2026, 12:00 midday UK time.
Apply via the SUSTAIN CDT website and follow the full instructions on the application page. Enquiries: [email protected].
Funding details
Fully funded PhD studentship. All PhD tuition fees are paid. A tax-free stipend is provided at UKRI rates to cover living costs. Includes a Research Training Support Grant of £3,000 per year (up to £12,000 total) for travel, training, and consumables, plus additional funding for outreach, dissemination, summer schools, research events, and development projects.
What's required
Honours degree minimum 2:1 in Microbiology, Genetics/Genomics, Biochemistry, or Biology, with strong expertise in bioinformatics and/or antimicrobial drug resistance. A master’s degree in AI or Machine Learning is desirable, as is a quantitatively or technically focused BSc/MSc dissertation or thesis. The student should be self-driven, curious, interdisciplinary, and willing to engage in departmental events, seminars, and public/policy engagement.
How to apply
Visit the SUSTAIN CDT application page for full instructions and submit the application through the provided website. Direct enquiries can be sent to [email protected].
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