Bioinformatic/machine learning/Omics

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Nom de la structure d'accueil

114 rue Edouard Vaillant
94805 Villejuif

Antonin Marchais
Email du/des contacts

We are seeking for curiosity driven postdoctoral researchers with strong background in Omics analysis and appetency to implement new machine learning strategies to decipher genetic program plasticity and tumor micro-environment composition in pediatric tumor along disease progression and/or after treatment failure. Department of Pediatric Oncology leads several large-scale integrative Omics/Clinical studies and currently develop its Bioinformatics capacities (2 Bioinformatician engineers, 1 PhD, 1 M2 and 1 team leader).

Molecular Profiling for Pediatric and Young Adult Cancer Treatment Stratification trial (MAPPYACTS) is a multicentric, prospective proof-of-concept study to estimate the immune contexture of pediatric tumor at relapse or treatment failure, and guide innovative treatments. More than 600 children participated to the trial and over the last years, clinical, IHC and multi-omics (RNA-seq, WES) data have been collected and processed to generate an outstanding resource to decipher the role of the microenvironment and response to new treatments in relapsing patients.

Postdoctoral fellow will first work to the development of new machine learning strategies to estimate the microenvironmental composition of pediatric tumors at relapse; taking advantage of large available public dataset at diagnostic. Benefiting from the mutational and copy number alterations detected in paired sample, we expect to associate the compositional modulation to specific genetic alterations. Initiatives to propose global and pathology specific projects will be supported by the team and candidate research proposals are welcome.

Researchers in Gustave Roussy Cancer Campus benefit from dynamic transversal and translational scientific environment with top-notch sequencing and HPC facilities.

Candidate profile
We are looking for candidate with excellent publication records, advanced skills in R or Python, expertises in statistics and machine learning as well as knowledge in Biology and Oncology.

2 years with possible extension.

Please email your CV and cover letter to

Equipe adhérente personne morale SFBI
Equipe Non adhérente