Research Fellow in Computational Metabolomics - School of Biosciences - 105468 - Grade 7

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Research Fellow in Computational Metabolomics – School of Biosciences – 105468 – Grade 7

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Client:

University of Birmingham

Location:

United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Reference:

38eedb64affa

Job Views:

8

Posted:

18.07.2025

Expiry Date:

01.09.2025

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Job Description:

Summary

• Develop, implement, and apply advanced reproducible computational tools and workflows to process, analyse, and interpret large-scale LC–MS-based comparative metabolomics datasets. These datasets, spanning a range of model organisms (. Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Daphnia magna, etc) and human cell lines, represent one of the world’s most comprehensive toxicological cross-species metabolomics resources.
• Drive the in-depth computational characterisation and annotation of metabolomes across diverse model organisms and human cell lines, using multistage fragmentation (MSⁿ) data and computational methods and approaches (. spectral matching, network-based approaches, and machine learning techniques, etc).
• Design and apply robust statistical analysis strategies to uncover biologically meaningful patterns from complex metabolomics datasets, support hypothesis testing, and enable cross-species comparisons of metabolic responses to chemical exposures or other perturbations.
• Actively promote and uphold FAIR (Findable, Accessible, Interoperable, Reusable) data practices, ensuring that all workflows, datasets, and code are transparent, well-documented, reproducible, and openly shared with the scientific community.
• The role also offers opportunities to contribute to teaching and training activities across undergraduate (Bachelor’s/BSc) and postgraduate (Master’s/MSc) programmes, mentor early-career researchers, and engage with dynamic, interdisciplinary networks involving academic, industrial, and regulatory partners.

Main Duties

• Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species and experimental conditions.
• Drive the characterisation and annotation of metabolomes using multistage fragmentation (MSⁿ) data, developing and employing a wide range of approaches and methods (. spectral matching, network-based approaches, and machine learning).
• Generate and maintain high-quality documentation, including detailed scientific reports and user guides for developed tools and workflows.
• Troubleshoot and resolve challenges affecting data quality, analysis workflows, or research outcomes, working independently or in collaboration with colleagues.
• Ensure that all computational tools, workflows, and data outputs are robust, version-controlled, and reproducible, adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles.
• Disseminate research findings through journal publications, conference presentations, workshops, internal reports, and web-based resources.
• Curate, manage, and submit metabolomics datasets to local and international data repositories, ensuring quality control and comprehensive metadata annotation.
• Contribute to the delivery of lectures and practical sessions for undergraduate (Bachelor’s/BSc) and postgraduate (Master’s/MSc) programmes (or similar), particularly in areas such as metabolomics, bioinformatics, and toxicology.
• Collaborate with interdisciplinary teams across academia and collaborators to advance the aims and objectives of projects such as PrecisionTox and PARC.
• Support the preparation of grant proposals and funding bids related to metabolomics, computational biology, and toxicology.
• Promote equality, diversity, and inclusion, and contribute to creating an open and welcoming research environment.
• Apply knowledge in a way which develops new intellectual understanding.
• Contribute to developing new models, techniques and methods.

Person Specification

Qualifications and Training

• PhD (awarded or near completion) or equivalent experience incomputational or mass spectrometry-based metabolomics, bioinformatics, or a related discipline.
• Demonstrated commitment to ongoing professional development in one or more of the above disciplines.

Technical Knowledge and Experience

• Strong expertise in the processing, analysis, and interpretation of LC–MS and/or MSⁿ data using open-source and commercial tools (. XCMS, MZmine, Compound Discoverer, GNPS, SIRIUS, etc).
• Proficiency in one or more programming languages (. R, Python).
• Experience with continuous integration and best practices in code development and maintenance (. Github/Gitlab, GitHub Actions, etc).
• Experience with machine learning or network-based approaches is desirable.
• Familiarity with FAIR data principles and experience in developing reproducible and well-documented computational workflows and analysis (. Jupyter, RMarkdown, Galaxy, Snakemake, or Nextflow).
• Knowledge of toxicology or human/environmental health would be an advantage.

Interpersonal and Teamwork Skills

• Excellent communication skills, including the ability to clearly present complex technical data to a range of audiences.
• Proven ability to work collaboratively within multidisciplinary and/or national and international teams.
• Experience mentoring students or early-career researchers is desirable.

Personal Attributes and Working Style

• Highly organised, with the ability to manage multiple tasks and priorities simultaneously.
• A high level of accuracy and attention to detail.
• Capable of working independently and proactively, including time management.
• Enthusiastic, adaptable, and able to contribute positively to a dynamic and fast-paced research environment.
• Knowledge of the protected characteristics of the Equality Act 2010, and how to actively ensure in day to day activity in own area that those with protected characteristics are treated equally and fairly.

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