Mots-Clés
metabolomique
python
R
chimie
Description
Détails de l’offre Research Fellow in Computational Metabolomics https://www.linkedin.com/feed/update/urn:li:activity:7343919935741460480/
et sur le site de l’université de Birmingham https://edzz.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_6001/job/7289
We are seeking an innovative Research Fellow in Computational Metabolomics to join the Centre for Environmental Research and Justice (CERJ) and the School of Biosciences at the University of Birmingham. You will be working in a vibrant, interdisciplinary research ecosystem, driving cutting-edge research at the intersection of metabolomics, human health, and toxicology. This exciting role offers the opportunity to contribute to major EU-funded research programmes, including PrecisionTox and PARC (Partnership for the Assessment of Risks from Chemicals), in close collaboration with the Phenome Centre Birmingham. These programmes are advancing the science and practice of chemical risk assessment and informing next-generation regulatory frameworks to better protect human health and the environment from toxic substances.
Specifically, the post holder will develop, implement, and apply advanced computational tools and reproducible workflows to interrogate large-scale, liquid chromatography–mass spectrometry (LC–MS)-based comparative metabolomics datasets spanning a range of model organisms (e.g. Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Daphnia magna, and others) and human cell lines. In addition, the role will involve the in-depth characterisation and annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community engagement.
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 (e.g. XCMS, MZmine, Compound Discoverer, GNPS, SIRIUS, etc).
Proficiency in one or more programming languages (e.g. R, Python).
Experience with continuous integration and best practices in code development and maintenance (e.g. 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 (e.g. Jupyter, RMarkdown, Galaxy, Snakemake, or Nextflow).
Understanding good practice in research data stewardship.
Knowledge of toxicology or human/environmental health would be an advantage.