To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development of novel analysis approaches and the interpretation of the outputs from these analyses.
The successful candidate will develop and implement bioinformatics pipelines to analyse thousands of bacterial and archaeal genomes, characterizing core and accessory gene dynamics across diverse phylogenetic scales. A key focus will be developing transformer models to capture patterns of prokaryotic evolution, including gene gain/loss events, horizontal gene transfer, and functional diversification within gene families.
You will apply statistical models and machine learning algorithms to identify associations between genomic variation and phenotypic traits, predict gene essentiality, and model evolutionary trajectories. The role involves using large language models as coding assistants for efficient pipeline development in Python, working with high-performance computing clusters, and implementing reproducible research workflows.
The position requires expertise in prokaryotic genomics, strong statistical and programming skills, and experience with modern machine learning approaches. You will analyse pangenome structure and dynamics, develop new computational methods for comparative genomics, and investigate the relationship between genomic flexibility and ecological adaptation.
Responsibilities include preparing manuscripts for publication, presenting findings at conferences, collaborating with experimental biologists, and contributing to grant applications. The post offers opportunities to work with international research groups and contribute to open-source bioinformatics tools. Experience with deep learning frameworks and transformer architectures applied to biological sequences would be advantageous.
You should have a PhD in bioinformatics, evolutionary biology, machine learning or similar.
The post fixed term and is available for up to 3 years. The preferred start date is 5 January 2026.
If you are still awaiting your PhD to be awarded you will be appointed at Grade 6, spine point 30. Upon written confirmation that you have been awarded your PhD, your salary will be increased to Grade 7, spine point 31.
Commitment to Diversity
The University of Liverpool is committed to enhancing workforce diversity. We actively seek to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds, individuals living with a disability, and members of the LGBTQIA+ community.