Mots-Clés
Bioinformatics
Computational biology
single-cell
brain tumor
machine learning
tumeur
cancer
Description
The Cavalli Lab is seeking a highly motivated scientist interested in deciphering brain tumor heterogeneity.
Institut Curie is one of the biggest European institutions for cancer research with strong interdisciplinary traditions. It also comprises a hospital specialized in cancer treatment, and therefore covers a continuum of expertise from fundamental research to patient care (https://institut-curie.org/).
The Cavalli Lab is part of the Computational Oncology Unit (U1331 INSERM, Mines ParisTech, Institut Curie) at Institut Curie, which consists of ~90 researchers and students. It is a very active and growing interdisciplinary team of bioinformaticians, biologists, physicians, mathematicians, statisticians, physicists, and computer scientists (U1331 Unit page ).
The Cavalli Lab (located at Institut Curie St-Cloud, west of Paris), investigates tumor heterogeneity, targeting clinically relevant questions. The goal of our genomic approaches is to explore clinically relevant aspects of brain tumor biology. We pursue this goal using patient samples profiling, investigating temporal and intra-tumoral/spatial heterogeneity as well as tumor/tumor microenvironment interactions in gliomas and pediatric embryonal brain tumors. Projects in the Cavalli lab are developed within a dynamic and collaborative environment with other researchers and clinicians at Institut Curie and national/international wet lab collaborators.
We seek a highly motivated and talented individual to tackle the complexity of tumor biology and intra-tumoral heterogeneity performing computational analysis of cutting-edge sequencing datasets. The successful candidate will be in charge of the analysis of single-nucleus RNA-seq/ATAC-seq and spatial transcriptomics data from brain tumor patient samples or models in close collaboration with experimentalists.
This project focus on deciphering tumor cell plasticity, including method evaluation and biological interpretation of the results. In addition, she/will contribute to a cross species pediatric tumor model comparison deciphering the tumor cell programs. Finally, he/she will investigate the metabolome in our IDH-mutant glioma tumors and perform data integration with other omics layers.
Qualification
The successful candidate should hold a recent PhD, have a track record of creativity in developing analytic strategies, and a strong foundation of knowledge in one or more of the following: genomics, cancer biology, statistics. Excellent computational skills with experience in single-cell data analysis and/or machine learning based method is highly desirable. Must be self-motivated, capable to work in autonomy and have evidence of scientific accomplishment via peer-reviewed publications. Excellent written and verbal communication skills in English and a team spirit are essential.
Responsibilities
- Drive and develop scientific projects focused on tumor heterogeneity
- Perform end-to-end data analysis (QC, processing, normalization, visualization, interpretation, etc)
- Analyse single-cell and spatial transcriptomics datasets
- Decipher metabolomic patterns
- Perform high dimensional data analysis and integrative ‘omic’ data analysis
Experience / Skills
- PhD in bioinformatics, statistics, or computer science with knowledge and interest in biology
- Experience working in a Unix environment and statistical analysis using R
- Experience with single-cell RNA-seq analysis
- Experience with machine learning based methods
- Understanding of cancer cell biology is an asset as well as experience of collaboration with biologists for solving concrete biological problems