|в Gustave Roussy Cancer Center (посмотреть профиль)
Постдок или научный сотрудник
|114 rue Edouard Vaillant
Integrative molecular signatures of metastatic patients
Research environment and project
Gustave Roussy, the premier European Cancer Centre, recruits a bioinformatics or computational biology postdoctoral fellow at Institut Hospitalo-Universitaire (IHU) PRISM led by Pr. Fabrice André. IHU PRISM is a joint project of Gustave Roussy, Université Paris-Saclay and other education/research institutions that aims at modeling cancer using integrative patient data for the development of novel precision medicine therapies.
The postdoctoral fellow will have access to a unique molecular sequence dataset (RNA and DNA) for hundreds of metastatic tumors. Her/his mission will be to identify heterogeneous molecular signatures associated to different clinical variables in order to improve understanding of the metastatic progression in a patient-specific context. Model building and validation will also exploit available public databases.
The successful candidate will be supported on the longer term to apply for CNRS/INSERM starting grants and positions in order to establish his/her own research group at Gustave Roussy.
Applicants should have doctoral experience in human genome/transcriptome data analysis and/or machine learning, with a desire and capacity to acquire complementary expertise, including in oncology. Valued skills for this position include: transcriptomics, germline or somatic DNA variation analysis, multi-omics integrative models, statistical classification, gene network and gene module analysis.
In order to apply to CNRS or INSERM starting grants, a solid doctoral and postdoctoral publication track record is necessary.
The postdoctoral fellow will be self-motivated and able to work independently and get actively involved with colleagues of the PRISM institute.
Ph.D. in bioinformatics, computational biology, biostatistics or machine learning.
Initial fixed-term 18-month contract, with possible extension. Salary dependents on experience.