Lead bioinformatician at the Spatial-Cell-ID spatial transcriptomics facility of Lyon
CDD · IR · 96 mois (renouvelable) Bac+8 / Doctorat, Grandes Écoles Ecole Normale Supérieure de Lyon · Lyon (France) 2000 to 3000 euros according to experience
Date de prise de poste : 3 janvier 2022
spatial transcriptomics, single cell, NGS, machine learning
The Spatial-Cell-ID facility of Lyon is seeking a highly talented Bioinformatician/Computational Biologist. The successful candidate will lead the data analysis efforts of our newly funded facility by developing methods and pipelines to analyse the biological data generated.
Spatial-Cell-ID is a new national spatial transcriptomics facility funded by the French “EquipEx+” excellence initiative and led by the École Normale Supérieure of Lyon (ENSL). It aims to study cellular identity and its spatial heterogeneity within tissues, organs, or biological systems in normal and pathological contexts, through very recent developments in spatial transcriptomics. Spatial transcriptomics technologies were elected “Method of the Year 2020” by the Nature Methods journal and are currently revolutionizing our ability to study complex biological systems. Spatial-Cell-ID offers national equipment for spatial transcriptomics that integrates imaging, sequencing, and data analysis technologies which in synergy will provide access to the transcriptome of any single cell within its native spatio-temporal environment. It hosts a comprehensible selection of technologies, including single-cell transcriptomics (10x Genomics), untargeted spatial transcriptomics (e.g., Slide-seq), and targeted spatial transcriptomics (e.g., MERFISH), associating state-of-the-art technological platforms of the University of Lyon. The data analysis hub of Spatial-Cell-ID will be located at the École Normale Supérieure of Lyon.
Role : The appointed candidate will coordinate the data analysis hub of Spatial-Cell-ID and assist the users in the analysis of their single cell and spatial data. She/he will be working at the ENS of Lyon, be a member of the Ghavi-Helm team at the IGFL (https://www.ghavihelmlab.com/), and closely collaborate with biologists and bioinformaticians to implement data analysis pipelines and develop new procedures as needed. She/he will communicate findings, coordinate skill transfers between labs, and provide training to project members as needed. She/he will be provided with extensive computational resources (workstations, High Performance Computing facility of the ENS de Lyon) and will benefit from the support of the bioinformatics community within Spatial-Cell-ID.
- PhD or equivalent experience in quantitative science (Bioinformatics, Computational Biology etc.).
- Good knowledge of one or more programming/scripting languages (e.g. Python, R, bash).
- Experience in omics data analysis, ideally in the context of single-cell and spatial technologies.
- Experience or the aptitude to quickly become proficient with the following techniques: untargeted spatial transcriptomics (e.g. Visium, Slide-Seq), targeted spatial transcriptomics (e.g. MERFISH), image analysis, machine learning methods for the analysis of biological data.
- Excellent communication and collaboration skills. English is the working language.
The employer : The École Normale Supérieure de Lyon is an elite French public higher education institution that trains professors, researchers, senior civil servants as well as business and political leaders. It is a symbol of French Republican meritocracy and it remains committed today to disseminating knowledge to the widest audience and to promoting equal opportunity. The ENSL brings together several laboratories at the cutting edge of science and working on different fields of Biology, Mathematics, Physics and Humanities.
Procédure : Applications should include a CV, a cover letter, and contact details for 3 referees to be sent to: Yad Ghavi-Helm (email@example.com). Please use the email subject “Spatial Cell ID”. For further information please contact the same address. Applications will be considered upon submission.
Date limite : 31 octobre 2021
Offre publiée le 16 septembre 2021, affichage jusqu'au 31 octobre 2021