Postdoc in Computational Medicine: AI & Multi-Omics for AML

Biopharma Careers; Gif-sur-Yvette; Sur place; EUR 42 000 - 60 000; Plein temps

Résumé du poste

Servier seeks a postdoctoral fellow to join our translational medicine research team, focusing on deciphering and modeling therapy resistance in Acute Myeloid Leukemia using AI-based approaches on multi-omic data.

You will help characterize intra-tumoral heterogeneity, benchmark methods in R or Python, and collaborate with R&D and IT to translate findings into publications and scientific insights.

Qualifications

• PhD in Bioinformatics, Biostatistics, or Data Science with single-cell omics expertise.
• Hands-on experience with single-cell sequencing technologies (e.g., scRNA-seq, proteogenomic).
• Proven experience developing data science (Machine Learning, AI) applications for translational research.
• Excellent skills in R and/or Python, including code sharing and versioning.
• Proficiency with Linux environments, HPC, and cloud computing.
• Scientific level English (written and spoken).

Responsabilités

• Self-document on state-of-the-art methods of interest for the project.
• Implement, benchmark, and share methodologies in R or Python for the project.
• Perform comprehensive multi-omic data analysis to characterize and model relapse-specific tumor clones.
• Identify biomarkers or predictive signatures associated with therapy resistance.
• Synthesize and interpret analyses in written reports in English.
• Share selected project outcomes through scientific publications, posters, and/or oral presentations.
• Collaborate with cross-functional teams (R&D, IT) to ensure integration of research efforts.

Connaissances

Single-cell omics

R & Python

ML/AI applications

Linux HPC Cloud

Scientific English

Formation

PhD in Bioinformatics/ Biostatistics/ Data Science

Outils

R

Python

scRNA-seq

HPC

Description du poste

Servier seeks a postdoctoral fellow to join our translational medicine research team, focusing on deciphering and modeling therapy resistance in Acute Myeloid Leukemia using AI-based approaches on multi-omic data.

You will help characterize intra-tumoral heterogeneity, benchmark methods in R or Python, and collaborate with R&D and IT to translate findings into publications and scientific insights.