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

Liverpool School of Tropical Medicine; Liverpool; On-site; GBP 34,000 - 48,000; Full time

Benefits offered by this job

Career development

Wellbeing support

30 days leave

Pension schemes

Cycle to work

Sports centre access

Family policies

Job summary

Liverpool School of Tropical Medicine is seeking an experienced Post Doctoral Research Associate to lead computational analyses of multi-omics data in resistance to malaria vectors. You will work within the Chemical Biology Group, Department of Vector Biology, to integrate transcriptomic, proteomic, genomic, and phenotypic data with insecticide uptake and metabolism studies.

You will develop reproducible Python/R workflows, apply statistical and interpretable machine-learning approaches, and

Qualifications

• PhD in bioinformatics, computational biology, genomics, proteomics, or related field with substantial computational experience.
• Strong experience analysing high-dimensional molecular data across omics types.
• Proficiency in Python and/or R with maintainable computational workflows.

Responsibilities

• Lead transcriptomic and proteomic analyses from QC to differential expression and enrichment.
• Integrate multi-omics data with phenotypic and uptake/metabolism evidence.
• Develop and apply ML approaches for multi-omics integration and resistance phenotype prediction.
• Design and interpret functional validation studies, including RNAi where appropriate.
• Contribute to reproducible workflows and data provenance across the team.

Skills

Bioinformatics

Computational biology

Genomics

Biostatistics

Data science

Python

R

Machine learning

Education

PhD in bioinformatics, computational biology, genomics, proteomics, systems biology, biological sciences, computer science, or closely related

Tools

Python

R

Linux/command-line

Job description

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.

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 where appropriate; contribute analytical or methodological content to research proposals, grants and fellowship applications.
• Share documented code and datasets through appropriate repositories in line with 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.

• Work with collaborators and core facilities to resolve data-quality issues, support colleagues and students, and transfer reproducible workflows across the team.

  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., PhD in bioinformatics, computational biology, genomics, proteomics, systems biology, biological sciences, computer science, or a closely related discipline, with substantial computational or bioinformatics experience.

Requirements

• PhD in bioinformatics, computational biology, genomics, proteomics, systems biology, biological sciences, computer science, or a closely related discipline, with substantial computational or bioinformatics experience.
• Strong experience analysing high-dimensional molecular data, with substantive expertise in at least one of quantitative proteomics or transcriptomics and the ability to work across complementary molecular data types
• Strong programming skills in Python and/or R, with practical Linux/command-line experience and the ability to develop maintainable, version-controlled and reproducible computational workflows.
• Strong quantitative judgement in experimental design and analysis, including quality control, normalisation, missing data, batch effects, confounding, statistical power, differential analysis, multiple-testing correction, exploratory analysis and appropriate visualisation.
• Ability to critically evaluate analytical outputs, uncertainty and biological plausibility, and to communicate findings clearly to experimental scientists to inform validation studies.
• Clear scientific writing and presentation skills, with the ability to contribute substantially to peer-reviewed publications and research proposals, with opportunities to lead outputs where appropriate.
• (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