Postdoctoral Bioinformatics Job – Liverpool School of Tropical Medicine

Job Description

Liverpool School of Tropical Medicine is seeking an experienced and motivated Post Doctoral Research Associate in Multi-Omics Bioinformatics and Computational Biology to join a multidisciplinary research programme investigating the molecular basis of insecticide resistance in malaria vectors.

Based within the Chemical Biology Group in the Department of Vector Biology, the successful candidate will lead computational analysis and integration of transcriptomic, proteomic, genomic, and phenotypic data. The role will involve robust statistical analysis and, where appropriate, interpretable machine-learning approaches.

Working closely with experimental scientists, the researcher will integrate molecular data with insecticide uptake, metabolism, and functional validation studies using laboratory and field-derived Anopheles populations. The research will support resistance monitoring, genomic surveillance, and the development of next-generation vector control products.

Key Responsibilities

• 1. Transcriptomic and Proteomic Analysis
• Lead transcriptomic analysis, from quality control and quantification through differential expression and functional enrichment.
• Account for population differences, selection history, batch effects, data quality, controls, missing data, and false discovery.
• Lead the analysis of quantitative and chemical or affinity-enrichment proteomics datasets.
• Integrate transcriptomic, proteomic, genomic, insecticide uptake, metabolism, and phenotypic evidence.
• Identify and rank candidate insecticide-resistance mechanisms and testable biological hypotheses.
• 2. Candidate Prioritisation and Functional Validation
• Develop an integrated molecular evidence framework to identify shared and compound-specific resistance mechanisms.
• Assess potential cross-resistance and prioritise candidates for experimental validation.
• Work with experimental and analytical colleagues to design and interpret functional, biochemical, and molecular validation studies, including RNAi-based approaches where appropriate.
• Evaluate evidence for candidate resistance markers and translate validated findings into priorities for genomic surveillance and resistance monitoring.
• Develop and evaluate statistical and, where appropriate, interpretable machine-learning approaches for multi-omics integration, feature selection, and prediction of resistance phenotypes.
• Apply sequence analysis or structural bioinformatics where informative.
• 3. Reproducibility, Data Management, and Research Outputs
• Develop version-controlled Python and/or R workflows with documented quality control, statistical methods, software versions, and data provenance.
• Contribute to curated datasets, figures, peer-reviewed manuscripts, reports, and presentations.
• Take opportunities to lead research outputs where appropriate.
• Contribute analytical or methodological content to research proposals, grants, and fellowship applications.
• Share documented code and datasets through appropriate repositories, following open-access, data-management, and confidentiality requirements.
• Work with collaborators and core facilities to resolve data-quality issues, support colleagues and students, and transfer reproducible workflows across the team.

Qualifications

• A PhD in Bioinformatics, Computational Biology, Genomics, Proteomics, Systems Biology, Biological Sciences, Computer Science, or a closely related discipline.
• Substantial computational or bioinformatics experience.
• Strong experience analysing high-dimensional molecular data.
• Substantial expertise in at least one of quantitative proteomics or transcriptomics, with the ability to work across complementary molecular data types.
• Strong programming skills in Python and/or R.
• Practical experience with Linux and command-line environments.
• The ability to develop maintainable, version-controlled, and reproducible computational workflows.
• Strong quantitative judgement in experimental design and analysis.

Experience

Multi-omics data integration.

Machine learning and interpretable predictive modelling.

Malaria research or vector biology.

Insecticide-resistance mechanisms and resistance surveillance.

Genomics, proteomics, and transcriptomics.

Sequence analysis and structural bioinformatics.

Skills Required

• Quality control and normalisation of biological datasets.
• Missing-data assessment, batch-effect correction, and confounding.
• Statistical power, differential analysis, and multiple-testing correction.
• Exploratory data analysis and appropriate data visualisation.
• Critical evaluation of analytical outputs, uncertainty, and biological plausibility.
• Communicating findings clearly to experimental scientists to inform validation studies.
• Scientific writing and presentations.
• Contributing to peer-reviewed publications and research proposals.

Additional Information

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