PhD Studentship: Algorithms and Methodology for Understanding Bacterial Plasmid Evolution

Project supervisor - Dr Katharina Huber Infectious diseases present a significant threat to the health of the UK population and economy with the overall annual costs estimated at £30bn each year. Bacteria form a key part of this threat, with the potential costs of ever-increasing levels of antimicrobial resistance (AMR) across many bacterial species likely to swamp these figures in the coming years. Key activities in countering bacterial threats are UK and international sequencing programs, which offer the potential to identify and monitor bacteria of particular concern. In recent years it has become apparent that in addition to genes underpinning AMR becoming more frequent within bacterial genomes, they are often achieving this through presence on bacterial plasmids. These are small sections of DNA additional to the main chromosome that can be transferred between bacteria, sometimes even between members of different species, and understanding how they have evolved poses formidable biological and computer sciences challenges.  If you are interested in being involved in developing cutting edge computer science tools and techniques to help address some of them and therefore contribute to the development of strategies that, in the long run, might allow us to develop ways to combat plasmid related AMR, then this project might be ideal for you. To provide you with the necessary skills to build a successful career at the interface between the biological sciences and computer sciences, you will be (i) developing methodology and software (including the potential for AI-based) to analyse large datasets, and (ii) supervised by experts working at the University of East Anglia and the UK Health Security Agency. In addition, you will also have the opportunity to attend relevant training opportunities at the European Bioinformatics Institute and the Earlham Institute. Prior biological knowledge is not required for the project and informal inquiries are welcome by the supervisors. The School of Computing Sciences ( https://www.uea.ac.uk/about/school-of-computing-sciences ) provides a vibrant research environment for conducting Computing and allied research and training. We collaborate with multi-national companies such as Apple, BT, the National Trust and Aviva, UK Health Security Agency (UKHSA), research institutes in the Norwich Research Park ( https://www.norwichresearchpark.com ), as well as other universities and industries in the UK and overseas. We are also members of the Turing University Network, a group of 65 UK universities working together to advance world-class research and build skills for the future. The successful candidate will also be expected to contribute to Tutor activities for laboratory support on our BSc and MSc Courses in Artificial Intelligence, Data Science, Computing Sciences and Cyber Security commensurate with their core expertise, within the working hours permitted for full-time Postgraduate Researchers. Over the past few years, the Wu group has developed deep learning approaches for extracting evolutionary and functional signals from large scale genomic datasets (2). Building on this expertise, this PhD project aims to develop AI based models to predict splicing sites and splicing alterations. In collaboration with Dr Alper Akay (UEA) and Prof. Yiliang Ding (JIC), the project will utilise deep learning and large language models to integrate genomic sequence analysis with RNA sequencing data from wild type and spliceosome mutant Caenorhabditis elegans and human cells (3). Entry requirements The standard minimum entry requirement is 2:1 in Computer Science or related subject area. Mode of study: Full-time Start date: 1 October 2026 £20,408. This PhD project is in a competition for a funded studentship. Funding comprises ‘Home’ tuition fees, an annual tax-free maintenance stipend (2026/27 rate £20,408) for a maximum of 3 years, and £2,000 per annum to support research training activities.