Ingénieur.e en génomique

SFBI; Créteil; Sur place; EUR 45 000 - 65 000; Plein temps

Résumé du poste

IMRB's MINT-Hep team in Créteil, INSERM U955, is seeking a highly motivated Research Engineer in Bioinformatics to conduct multi-omics, single-nucleus RNA-seq and spatial transcriptomic analyses in liver cancer.

The successful candidate will test, deploy and maintain computational tools, develop reproducible pipelines, and collaborate with biologists and clinicians to translate biological questions into computational analyses.

Qualifications

• Training in bioinformatics with a biology focus or cancer research.
• Experience working in a Unix/Linux environment.
• Good knowledge of R and statistical data analysis.
• Experience with genomic and/or transcriptomic data analysis.
• A background in cancer genomics, tumor biology or immuno-oncology is a plus.
• Ability to work collaboratively across disciplines.

Responsabilités

• Test, improve and deploy computational tools for genomic and transcriptomic analyses.
• Develop and maintain reproducible pipelines for bulk RNA-seq, WES/WGS, single-nucleus RNA-seq and spatial transcriptomics.
• Perform end-to-end data analysis: QC, preprocessing, normalization, statistics and visualization.
• Develop approaches for multi-omic and spatial data integration.
• Maintain and document analysis pipelines and code.
• Collaborate with biologists, pathologists and clinicians to translate questions into analyses.

Connaissances

Unix/Linux

R and statistics

Multi-omics data integration

Collaborative mindset

Formation

Bioinformatics training

Outils

R

Description du poste

Description

The MINT-Hep team at the Institut Mondor de Recherche Biomédicale (IMRB, INSERM U955) is seeking a highly motivated Research Engineer in Bioinformatics to conduct multi-omics, single-cell and spatial transcriptomic analyses in liver cancer.

The IMRB is a multidisciplinary biomedical research institute located in Créteil, in the Paris area, bringing together basic, translational and clinical research. Our team works in close collaboration with clinicians, pathologists and experimental researchers to better understand the molecular mechanisms underlying liver tumor development, progression and response to treatment.

The MINT-Hep team focuses on the molecular characterization of primary liver tumors, particularly hepatocellular carcinoma and other liver tumor subtypes. Our research combines genomic, transcriptomic and spatial approaches with detailed clinicopathological annotation to investigate tumor heterogeneity, tumor–immune interactions, hepatocarcinogenesis and biomarkers of disease progression and therapeutic response.

The successful candidate will participate in several research projects aiming to characterize the molecular landscape of liver tumors through the integration of bulk RNA-seq, WES/WGS, single-nucleus RNA-seq and spatial transcriptomic data. The position will involve both the application of established bioinformatics workflows and the development of new analytical strategies for multi-omic and spatial data integration.

Responsibilities

Test, improve and deploy computational tools for genomic and transcriptomic analyses.

Develop and maintain reproducible pipelines for bulk RNA-seq, WES/WGS single-nucleus RNA-seq and spatial transcriptomics.

Perform end-to-end data analysis, including quality control, preprocessing, normalization, statistical analysis and visualization.

Develop innovative approaches for multi-omic and spatial data integration.

Maintain and document analysis pipelines, scripts and computational tools developed within the team.

Interact closely with biologists, pathologists and clinicians to translate biological and clinical questions into appropriate computational analyses.

Experience / Skills

Training in bioinformatics with a strong interest in biology and cancer research; alternatively, a biological background combined with solid computational skills.

Experience working in a Unix/Linux environment.

Good knowledge of R and statistical data analysis.

Experience with genomic and/or transcriptomic data analysis.

A conceptual background in cancer genomics, tumor biology or immuno-oncology would be an asset.

Ability to work collaboratively in a multidisciplinary environment and to communicate with both computational and experimental researchers.