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Sharon Huws

Professor at Chemistry & Chemical Engineering

Queen's University Belfast

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United Kingdom

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Research Interests

Artificial Intelligence

10%

Biocatalysis

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Chemistry

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Pharmaceutic

10%

Environmental Science

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Biology

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Positions1

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David Rooney

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Queen’s University Belfast

AI-Driven Discovery and Engineering of Novel Enzymes for Biogas Mitigation and Utilisation

A fully funded PhD studentship is available at Queen’s University Belfast in the groups of Prof David Rooney, Prof Sharon Huws, and Dr Meilan Huang, as part of the BioAID Doctoral Training Programme. This cross-institutional initiative includes world-leading experts from Queen’s University Belfast, University of Manchester, University of Edinburgh, and University of Bristol. BioAID aims to train the next generation of scientists in Artificial Intelligence and data-driven approaches for translational biocatalysis, addressing critical needs in sustainable biotechnologies. The programme equips PhD students with advanced expertise in enzyme science, machine learning, enzyme engineering, and synthetic biology. Students will undertake interdisciplinary, co-supervised projects across biocatalysis and AI, supported by national computing infrastructure, hands-on laboratory training, and strong academic/industry partnerships through co-designed projects and placements. Structured cohort training and tailored professional development are delivered by partner institutions. This project focuses on developing a machine learning-guided, automated platform for the discovery and engineering of enzymes involved in methanogenesis metabolic pathways, with particular emphasis on methyl-coenzyme M reductase (MCR) and key dehydrogenases. Metagenomic data from farm microbial communities will be analysed using machine learning models to identify and functionally annotate enzymes contributing to methane production. Machine learning approaches will guide the selection of enzyme variants with improved catalytic efficiency, stability, and specificity from small experimental datasets. The resulting platform will facilitate adaptive bioprocess optimisation and integration with laboratory automation, providing scalable biotechnological solutions for methane mitigation and utilisation in sustainable agriculture. The studentship is fully funded for 48 months, covering tuition fees and an annual stipend at the UKRI rate (£20,780 per annum for 2025-26), subject to final confirmation of BBSRC funding. The scheme is open to UK and international students. Applicants must fulfil Queen’s University Belfast entry requirements, hold (or expect to achieve) a First Class or 2:1 UK honours degree (or international equivalent), and ideally hold a master’s-level qualification at merit or distinction. Flexible study arrangements, including part-time options, may be considered depending on the project and funder. Queen’s University Belfast is committed to equality, diversity, and inclusion, actively encouraging applicants from diverse backgrounds and career paths. Applications from those returning from a career break or other roles are supported. To apply, submit your application via the QUB Direct Application portal. Include the project title, supervisor name, and contact details of two referees. Ensure all required documents are submitted at the time of application. Incomplete applications will not be considered. For queries, contact [email protected]. It is strongly recommended to contact the supervisors before applying (email: [email protected]).

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