Job offer
Organisation/Company Institut Curie Department U1331 – Computational Oncology Research Field Biological sciences » Biology Computer science » Programming Other Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Positions Postdoc Positions Application Deadline 30 Apr 2026 - 23:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 4 May 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
The Cavalli Lab is seeking a highly motivated scientist interested in deciphering brain tumor heterogeneity.
Duration: 24 months, starting immediately
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 ( ). 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 individualto 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
Application
Applicants should send (1) a cover letter describing previous work, interests, and career goals, (2) CV, and (3) contact information of three referees to Dr. Cavalli at (with "Postdoc 2026" in the subject of your email)
Where to apply
Requirements
Research Field Computer science » Other Education Level PhD or equivalent
Skills/Qualifications
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
Languages ENGLISH Level Good
Research Field Biological sciences » BiologyComputer science » Programming Years of Research Experience 4 - 10
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