Mots-Clés

metabolomics

Description

The Institut Pasteur is a private, non-profit foundation. Its mission is to help prevent and treat diseases, mainly those of infectious origin, through research, teaching, and public health initiatives. As part of the creation of a new technological core facility (https://research.pasteur.fr/en/team/metabolomics-core-facility/) for developing non-targeted/targeted metabolomic/lipidomic approaches, the Institut Pasteur is looking for a bioinformatician / biostatistician (research engineer) for (i) the development of tools for compound annotation and (ii) the interpretation (e.g. using network theory) of metabolomics/lipidomics data.

Tasks and responsibilities:

• Applying and developing methods and computational tools to annotate known and unknown compounds coming from experiments (HRMS, MS/MS and MSn) performed at the platform
• Applying and developing methods and computational tools (from metabolic networks, …) to biologically interpret experimental results
• Building databases characterizing the compounds and developing tools to query and populate them as experiments are performed and novel data are generated
• Ensuring reproducibility and sustainability of the developed methods/tools
• Guaranteeing the use of state-of-the art metabolomics methods/tools
• Performing data treatment and analysis in close collaboration with the engineers of the platform and the research units of the Institute
• Actively engaging in the platform’s strategy to meet the scientific needs Institut Pasteur
• Delivering training courses in bioinformatics/biostatistics applied to metabolomics
• Writing scientific articles and participating in conferences to enhance the developed methods and tools
• Joining scientific networks dedicated to metabolomics

Candidate’s profile:

• PhD or master/engineer degree in chemoinformatics, bioinformatics and/or mathematics/biostatistics with experience in the use or development of networks (e.g. metabolic or molecular)
• Solid knowledge of statistical tools for the data exploration, modeling, inference and visualization. Knowledge in AI learning methods (neural networks for machine/deep learning) for spectra analysis and for compound annotation will be appreciated
• Solid knowledge of R and/or Python programing languages, use of bash, and knowledge of other languages like C/C++
• Strong interest in reproducibility and versioning
• Good writing/speaking communication skills in English (B2/C1)
• Rigor, pedagogy, scientific curiosity and interest in biology
• Flexibility and ability to work in a multidisciplinary environment and on multiple scientific projects
• Good communication and interpersonal skills