Postdoctoral Research Associate (Computational Vaccine Discovery)

Salary: £40,704- £46,970 per annum

Contract: Full time, Fixed term for 12 months

Location: Liverpool (Hybrid, 3 days per week on site)

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 MalariaGEN 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.

In This Role, You Will Have The Opportunity To

• Build the core decision platform at scale. Develop 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.