Learning Phage Genome Organisation with Predictive and Generative Models

This PhD project at the University of Bath offers an exciting opportunity to explore the organisation of phage genomes using cutting-edge predictive and generative deep learning models. Phages, viruses that infect bacteria, are renowned for their genomic modularity, which enables rapid evolution, host adaptation, and significant therapeutic potential. Despite their importance, the design principles underlying phage genome organisation remain largely unknown. The research will leverage recent advances in modelling bacterial, plasmid, and viral systems at both the protein and DNA levels. The successful candidate will extend language-model frameworks into generative models that learn the structural and functional dependencies across phage genomes. The project will focus primarily on phage systems, with insights drawn from plasmid and bacterial genomic contexts. Collaboration with the Iqbal Lab (Bath) and Finn Lab (EMBL-EBI) will provide access to expertise and large-scale datasets for model training, benchmarking, and interpretation. Key research areas include representation learning of bacterial and phage genomic structure and functional modules, generative modelling of realistic phage genomic variants constrained by host and functional context, and evolutionary interpretation to identify AI-derived design rules linking genome structure to ecological fitness. All research will be computational, with optional wet-lab validation of model predictions conducted by collaborators under independent biosafety approvals. The project is funded through the University of Bath URSA competition, with studentships covering tuition fees, a generous stipend (£20,780 p/a in 2025/6), and access to a training support budget for 3.5 years. The studentship is open to both Home and exceptional International students, though international applicants should note that funding does not cover relocation, visa, or UK healthcare surcharge costs. Applicants should hold, or expect to receive, a First Class or good Upper Second Class UK Honours degree (or equivalent) in a relevant subject. A master's qualification is advantageous. Candidates with backgrounds in computational biology, bioinformatics, computer science, data science, or related quantitative disciplines are strongly encouraged to apply. Prior experience in AI for biological data, machine learning, or large-scale genomic datasets is beneficial but not required. Non-UK applicants must meet the programme’s English language requirement by the application deadline. To apply, submit a formal application via the University of Bath’s online application form for a PhD in Biology before the deadline. In the 'Funding your studies' section, select 'University of Bath URSA' as the studentship, and in the 'Your PhD project' section, quote the project title and lead supervisor's name. Informal enquiries are encouraged and should be directed to Dr Jiawei Wang ([email protected]). Applications may close earlier than the advertised deadline if a suitable candidate is found, so early contact and submission are recommended. The University of Bath values diversity and inclusion, welcoming applications from under-represented groups. If you have circumstances that have affected your educational attainment, you are encouraged to mention them in your application.