Postdoctoral Research Associate (Computational Vaccine Discovery)

Liverpool School of Tropical Medicine; Liverpool; Hybride; GBP 40 000 - 48 000; Plein temps

Avantages offerts par ce poste

30 days annual leave

Pension schemes

Cycle to work scheme

Staff membership to university sports

Career development opportunities

Résumé du poste

Liverpool School of Tropical Medicine is seeking a Postdoctoral Research Associate (Computational Vaccine Discovery) to advance a malaria-focused decision platform. The role blends research with software and data engineering and involves collaboration with international vaccine researchers.

The successful candidate will develop scalable pipelines for peptide evaluation, with emphasis on MHC predictions, multi-omics integration, and evidence-based prioritisation, within a hybrid on-site/remote

Qualifications

• In-depth quantitative or life-science training with computational focus.
• Strong coding and software-engineering practices are required.
• Experience handling high-dimensional biological datasets is essential.
• Background in ML, statistics and model evaluation is desired.
• Proven ability to build reproducible pipelines in HPC/cloud environments.

Responsabilités

• Build scalable, documented local and cloud workflows for peptide evaluation and ranking.
• Integrate evidence layers and calibrate predictions using experimental data.
• Develop transparent scoring, filtering and prioritisation methods.
• Collaborate with international teams and produce reusable research outputs.

Connaissances

PhD in quantitative or life-science

Python programming

Large dataset analysis

Statistical analysis & ML

Reproducible workflows & HPC

Formation

PhD or equivalent experience

Outils

SQL

DuckDB

Description du poste

Postdoctoral Research Associate (Computational Vaccine Discovery)

Contract: Full time, Fixed term for 12 month

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 (https://www.malariagen.net/article/malariagen-awarded-phase-3-gates-foundation-grant-to-accelerate-malaria-vaccine-translation/), as well as exploring some of our previous work in this area (https://www.biorxiv.org/content/10.64898/2025.12.15.694343v2.full).

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.

The Successful Candidate Will Have

• 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.

It would be useful if you also have experience in one or more of:

• Proteomics, transcriptomics, single-cell or lifecycle-stage expression data, multi-omics integration, or biological knowledge graphs.
• Experience with relational database design, SQL, and modern analytical database technologies (particularly DuckDB), including building large, well-structured, queryable data resources for scientific research.
• Protein sequence or structure methods, protein language models, structural bioinformatics, or machine-learning approaches for protein and peptide analysis.

(For a full list of essential and desirable criteria please refer to the job description and person specification)

Additional Benefits Of Joining LSTM

• Access to support for your career growth through a variety of internal and external learning and development opportunities
• Employee Assistance Platform offering a range of wellbeing initiatives and support, in addition to high-street discount offers
• 30 days annual leave, plus 8 UK bank holidays, in addition to 6 Christmas closure days
• Generous occupational pension schemes including USS (Universities Superannuation Scheme) and NHS pension schemes (subject to eligibility)
• Government backed "cycle to work" scheme
• Affiliated, discounted staff membership to the University of Liverpool Sports Centre
• A range of enhanced family friendly policies