Postdoctoral Research Associate (Computational Vaccine Discovery)

1000scholars; Liverpool; On-site; GBP 60,000 - 90,000; Full time

Job summary

1000scholars is recruiting a curious, rigorous and delivery-minded scientist to turn the complexity of vaccine design into a usable decision platform at the Liverpool School of Tropical Medicine. This full-time, 12-month FTC role focuses on T-cell antigen and peptide prioritisation within the MalariaGEN team.

You will develop scalable, reproducible computational workflows, integrate diverse data sources, and collaborate with international researchers to generate actionable insights and

Qualifications

• PhD in a relevant quantitative or life-science discipline or substantial equivalent experience.
• Strong programming skills, preferably Python, with sound software-engineering practices.
• Experience analyzing or managing large, complex biological datasets.
• Experience with statistical analysis, machine learning, and model evaluation.
• Experience building reproducible workflows and analytical pipelines in HPC or cloud environments.

Responsibilities

• Build the core decision platform at scale with scalable, tested local and cloud workflows for peptide evaluation and predictions.
• Integrate evidence across multiple criteria including antigen presentation, population coverage, and conservation.
• Turn data and experiments into decisions with transparent scoring and prioritisation methods.
• Collaborate with international researchers to provide analyses, support, and reusable pipelines and documentation.

Skills

Python

Data analysis

Machine learning

Workflow automation

Cloud computing

Education

PhD in quantitative or life-science

Tools

HPC

Cloud platforms

Job description

Contract: Full time, FTC for 12 months

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 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.

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

Competitive