Postdoctoral Research Associate
2 weeks ago
OverviewTo 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.ResponsibilitiesPrepare manuscripts for publication and present findings at conferences.Collaborate with experimental biologists and contribute to grant applications.Develop and implement bioinformatics pipelines for large-scale prokaryotic genomics analysis.Explore core and accessory gene dynamics across diverse phylogenetic scales.Develop transformer-based models to capture patterns of prokaryotic evolution (gene gain/loss, horizontal gene transfer, functional diversification).Apply statistical and machine learning methods to link genomic variation with phenotypes and to model evolutionary trajectories.Utilize large language models as coding assistants to enhance pipeline development in Python; work on HPC clusters and maintain reproducible workflows.QualificationsPhD in bioinformatics, evolutionary biology, machine learning or a related field.Expertise in prokaryotic genomics, strong statistical and programming skills, and experience with modern ML approaches.Experience with deep learning frameworks and transformer architectures applied to biological sequences is advantageous.Contract and Start DateThe position is fixed-term and available for up to 3 years.The preferred start date is 5 January 2026.Commitment to DiversityThe 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. #J-18808-Ljbffr
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