Postdoctoral Research Associate (Computational Vaccine Discovery)

Liverpool-School-of-Tropical-Medicine; Liverpool; Hybrid; GBP 41,000 - 47,000; Full time

Benefits offered by this job

30 Days holiday

Bank Holidays

Christmas closure days

Pension schemes (USS/NHS)

Cycle to work

Sports Centre membership

Family friendly policies

Job summary

Liverpool School of Tropical Medicine seeks a Postdoctoral Research Associate (Computational Vaccine Discovery) to develop a scalable decision platform for malaria peptide evaluation and antigen discovery. The role blends computational research with software and data engineering and involves close collaboration with vaccine scientists.

You will build scalable workflows, integrate diverse data, and iteratively improve prioritisation using ML and statistics, contributing to publications and

Qualifications

• PhD or equivalent in quantitative or life-science field.
• Strong Python programming and software engineering practices.
• Experience with large, high-dimensional datasets.
• Experience with statistics, machine learning, model evaluation.

Responsibilities

• Build scalable, tested local and cloud workflows for malaria peptide evaluation and predictions.
• Integrate evidence layers (HLA coverage, conservation, expression) to improve predictions.
• Develop transparent scoring, filtering and optimisation for candidate prioritisation.
• Collaborate with international partners to produce reusable pipelines, datasets and documentation.

Skills

Python

Machine learning

Data analysis

Education

PhD or equivalent

Tools

DuckDB

SQL

HPC

Job description

Postdoctoral Research Associate (Computational Vaccine Discovery)

Reference: OCT20262683

Expiry date: 23:59, Sun, 1st Nov 2026

Location: Liverpool

Salary: Competitive

Benefits: 30 Days holiday + Bank Holiday and additional Christmas closure days

Postdoctoral Research Associate (Computational Vaccine Discovery)
Salary: £40,704- £46,970 per annum
Full time, Fixed term for 12 month ( Extension of the post may be possible, subject to funding)
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.

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