Postdoctoral Research Associate; Computational Vaccine Discovery

We are recruiting a curious, rigorous and delivery-minded scientist to help turn the complexity of vaccine design into a usable decision platform. Malaria remains one of the world’s most important infectious diseases, but the search space for new peptide-based vaccines is enormous. A single parasite proteome can generate millions of possible peptides, and the strongest candidates must satisfy multiple constraints simultaneously

You will join an interdisciplinary malaria research programme at the Liverpool School of Tropical Medicine and work within the Malaria GEN team, collaborating with international vaccine researchers and making use of large-scale computational resources. The initial focus will be T-cell antigen and peptide prioritisation, with opportunities to contribute to broader computational antigen discovery as the programme develops.

You will help shape the computational architecture, scientific methods and evidence model behind a platform designed to support real experimental decision-making. The role combines research with software and data engineering, alongside close collaboration with vaccine scientists. It offers the opportunity to build something ambitious yet practical at the intersection of malaria genomics, immunology, machine learning and translational vaccine research.

You can learn more about this opportunity by reading our blog post about the project (), as well as exploring some of our previous work in this area (https://www.biorxiv.org/content/10.64898/.694343v2.full).

• Build the core decision platform elop scalable, tested and documented local and cloud workflows for malaria peptide evaluation, MHC class I and II predictions, multi-omics integration, ranking and candidate-set optimisation across very large datasets.
• Integrate evidence and improve prediction. Combine antigen-presentation methods with HLA population coverage, parasite variation, conservation, expression, and other evidence layers while calibrating predictions with experimental data and quantifying model uncertainty and disagreement.
• Turn data and experiments into decisions. Develop transparent scoring, filtering and optimisation approaches that help collaborators compare candidates and design experimentally tractable peptide sets; ingest assay results, assess prediction performance and iteratively improve prioritisation methods.
• Collaborate and create reusable research outputs. Work with international collaborators to provide analyses, technical support and help with scientific interpretation, while producing reusable pipelines, structured datasets and databases, technical documentation, scientific reports, presentations and peer-reviewed publications.

• A PhD in a relevant quantitative or life-science discipline, or equivalent substantial experience delivering advanced computational research or data-science projects.
• Strong programming skills, preferably in Python, together with sound software-engineering practices.
• Experience analysing or managing large, complex biological, biomedical or similarly high-dimensional datasets.
• Experience with statistical analysis, machine learning, quantitative method development and model evaluation.
• Experience building reproducible workflows and analytical pipelines, ideally using HPC or cloud computing environments.

• Proteomics, transcriptomics, single-cell or lifecycle-stage expression data, multi-omics integration, or biological knowledge graphs.
• Experience with…