Post-Doc

Job description

Description de l'emploi

Oncology – Bioinformatics – Post-Doc (All Gender)

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Le contenu du poste est libellé en anglais car il nécessite de nombreuses interactions avec nos filiales à l’international, l'anglais étant la langue de travail.

CONTEXT

Sanofi, Gustave Roussy, Inserm, Institut Polytechnique de Paris and the University of Paris-Saclay are committed to developing personalized medicine in France through a patient-centered oncology cluster – the Paris Saclay Cancer Cluster. The Paris Saclay Cancer Cluster, a center bringing together key players in oncology innovation. This project, which is unique in Europe, will bring together the best scientific, human and technological expertise to shape the future of personalized medicine and accelerate the discovery of new customized cancer treatments. Within ten years, the objective is to be able to offer rapid diagnosis at the patient’s bedside, including disease modeling and the construction of an individualized and personalized therapy.

The candidate will work in multi-disciplinary environment on cross-functional projects. He/she will also ensure the transition of research programs, goals and milestones execution with IGR partners, providing scientific and technical expertise.

The successful candidates will perform analysis of CRISPR-screen data coming from high throughput screening approaches on PDX and patient-derived lung cancer cell lines sensitive or resistant to targeted therapy treatment with TK inhibitors (EGFRi, ALKi). The aim of this project is the identification of new targets for which no pharmacological agent is yet available and whose inhibition may prevent/delay resistance to therapy. The successful candidates are asked to specifically look for new targets from bypass signaling pathways and targets in the rare drug tolerant persister cancer cells. The successful candidate will closely interact with two post-docs, involved in the wet lab part of the CRISPR-screen, who were  already recruited.

Moreover, computational biologist/data scientist will be involved in analysis of data coming from Single cell,  CITE-seq, scATAC-seq, bulk-RNA-seq data analysis in order to capture the cancer cells heterogeneity and understand the impact of the tumor microenvironment on development of drug resistance.

In this exciting role as a part of the R&D (SANOFI) the candidate will be embedded within U981 INSERM Unit in Gustave Roussy. In this hands-on role the successful candidate will work to design high content experiments, to manage data, and to use advanced analytical and computational techniques to identify new targets, treatments combinations and data-driven biomarkers for lung cancer.

KEY RESPONSIBILITIES

·  He/she will perform analysis of CRISPR-screen data  to identify genes involved in resistance to TK inhibitors (EGFRi, ALKi);
·  He/she will perform Single-cell, CITE-seq and scATAC-seq data analysis, including data normalization, statistical analysis, Cell Type/State Analysis, Cell to cell interaction, Trajectory inference;
·  Analyze and utilize best practice for bulk RNA-Seq and Single cell data analysis;
·  He/she will address novel analytical challenges and will automate established pipelines to perform them in a robust manner;
·  Proactively engages with project teams to define crisp scientific questions that can be addressed with appropriately powered computational strategies;
·  Applies scientific expertise and judgement to interpret and present results,

in order to support research projects.

PROFILE

Educational requirements :

·  Ph.D. in a bioinformatics, computational biology, statistics or bioinformatics related fields
·  A minimum of 3-years research (academia or industry) experience

Specific skills and competencies:

·  An ideal candidate will be collaborative, self-directed and possess the following competencies;
·  Strong experience in NGS data analysis and pipeline development, statistics and data visualization;
·  Strong expertise in analyzing and integrating CRISPR-screen, bulk RNAseq, scRNA-seq, scATAC-seq and CITE-seq data;
·  Prior expertise in oncology, immune-oncology or immunology;
·  Familiarity with the reference datasets for gene expression, mutations, and related platforms (TCGA/GTEX/Human Cell Atlas);
·  Proficiency in scripting programming (R/Python/bash) and in statistical analysis;
·  Extensive experience working with genetics data is a plus;
·  Experience with machine learning/deep learning techniques is a plus;
·  Demonstrated contributions to multi-disciplinary collaborations;
·  Strong written and verbal communication skills;
·  Fluent in English (writing and speaking);
·  The candidate must demonstrate dynamism, autonomy, excellent communication and organization skills, rigor and agility, strong interpersonal skills, and team spirit;
·  Ability to work transversally across sites and functions.

CONTACT

Please submit a cover letter summarizing your past experiences and CV including the names and contact information of 2 references via email to Sergey Nikolaev and Galina Boldina.

sergey.nikolaev@gustaveroussy.fr

galina.boldina@sanofi.com

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