Post Doctoral Research Associate in Multi-Omics Bioinformatics and Computational Biology

Location:

Liverpool, UK

Full-time, fixed-term appointment to 31st December 2027

Post Doctoral Research Associate role in Chemical Biology Group, Department of Vector Biology, Liverpool School of Tropical Medicine

Leading computational analysis and integration of multi-omics data

Experience in bioinformatics, computational biology, genomics, biostatistics, or data science required

Strong skills in R and/or Python essential

Opportunities to contribute to research outputs and propose grants

Access to career growth and wellbeing initiatives at LSTM

Full-Time, Fixed-term appointment to 31st December 2027

We are seeking an experienced and motivated Post Doctoral Research Associate to join a multidisciplinary programme investigating the molecular basis of insecticide resistance in malaria vectors.

Based within the Chemical Biology Group, Department of Vector Biology, at the Liverpool School of Tropical Medicine, you will lead computational analysis and integration of transcriptomic, proteomic, genomic and phenotypic data, using robust statistical and, where appropriate, interpretable machine-learning approaches.

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

We are looking for a candidate with a background in bioinformatics, computational biology, genomics, biostatistics or data science, with experience analysing biological datasets and strong skills in R and/or Python. Experience in multi-omics, machine learning, malaria or vector biology would be advantageous.

Key responsibilities will include:

Transcriptomic and proteomic analysis

• Lead transcriptomic analysis from quality control and quantification through differential expression and functional enrichment, using appropriate comparators and accounting for population, selection history and batch effects, including assessment of data quality, enrichment, controls, missing data and false discovery.
• Lead analysis of quantitative and chemical/affinity-enrichment proteomics datasets
• Integrate transcriptomic, proteomic, genomic, insecticide uptake, metabolism and phenotypic evidence to identify and rank candidate resistance mechanisms and testable biological hypotheses.

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 a tractable set of 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.
• Assess evidence supporting 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; use sequence analysis or structural bioinformatics where informative.

Reproducibility, data management and research outputs

• Develop version-controlled Python/R workflows with documented quality control, statistical methods, software versions and data provenance.
• Contribute substantially to curated datasets, figures, peer-reviewed manuscripts, reports and presentations, with opportunities to lead outputs…