M2 or PhD (funding secured) : Multi-Omics Profiling and AI-Driven Reprogramming

SFBI; Toulouse; Sur place; EUR 13 000 - 20 000; Plein temps

Avantages offerts par ce poste

PhD funding

Research environment

Résumé du poste

SFBI in France invites a motivated M2 internship or PhD candidate to join a project on multi-omics profiling and AI-driven reprogramming to enhance regenerative medicine. You will integrate scRNA-seq, bulk transcriptomics, and epigenomics data across adipose and muscle tissues.

Proficiency in R or Python, Git, and high-throughput bioinformatics tools is expected. The role offers funded PhD opportunities and a collaborative environment at the forefront of cell therapy research in Toulouse.

Qualifications

• M1/M2 in Bioinformatics, Computational Biology, Systems Biology, or Data Science.
• Strong programming background in R and/or Python with Git experience.
• Experience with high-throughput bioinformatics tools (e.g., Seurat/Scanpy for scRNA-seq, multi-omics integration).

Responsabilités

• Integrate published and in-house multi-omics datasets across adipose and muscle tissues.
• Identify pharmacological targets and molecules using AI-based approaches.
• Translate multi-omics patterns into biological mechanisms for reprogramming ASCs.

Connaissances

Python

R

Git

Machine learning

Formation

M1/M2 Bioinformatics

Outils

Seurat/Scanpy

Description du poste

M2 or PhD (funding secured) : Multi-Omics Profiling and AI-Driven Reprogramming

single cell RNA seq single cell ATAC seq multiomics in silico pharmacological screening

Description

Multi-Omics Profiling and AI-Driven Reprogramming Strategies to Enhance Regenerative Medicine Cell Therapy

Position: M2 Internship / PhD Opportunity
Mesenchymal stem cells (MSCs)-and in particular Adipose-derived Stem/Stromal Cells (ASCs) due to their clinical accessibility-hold immense promise for regenerative medicine and cell therapy. However, clinical translation remains hampered by cell heterogeneity, inconsistent efficacy, and a lack of standardized characterization. Recently, our group identified a distinct, highly active subpopulation of ASCs from subcutaneous adipose tissue (ScAT) that actively migrates to injured muscle to drive tissue repair. To turn these insights into an actionable therapeutic strategy, we aim to map the molecular signatures of this potent subset and develop computational strategies to reprogram heterogeneous or suboptimal clinical ASCs into standardized, high-potency cell therapy products.
To decipher what distinguishes these mobilized ASCs from others, the candidate will integrate published and in-house multi-omics datasets (e.g., scRNA-seq, bulk transcriptomics, epigenomics) across adipose and muscle tissues during aging and regeneration.
Leveraging these multi-omics insights alongside advanced bioinformatics workflows and computational/AI tools, the candidate will identify potential pharmacological targets and molecules capable of reprogramming suboptimal or clinical ASCs into high-potency regenerative phenotypes.
This research will yield fundamental insights into cell fate decisions particularly during aging and establish a baseline for novel, targeted cell-therapeutics and rejuvenation strategies.
Candidate Profile & Required Skills
Education: M1 or M2 (or equivalent) in Bioinformatics, Computational Biology, Systems Biology, or Data Science. The position is open to both M2 internship and PhD candidates. Funding is available for a PhD position, and outstanding M2 candidates will be particularly encouraged to apply.
Technical Skills: Strong background in computer programming (R and/or Python), version control (Git), and routine experience with high-throughput bioinformatics tools (e.g., Seurat/Scanpy for scRNA-seq, multi-omics integration frameworks).
Interests & Mindset: An interest in applying modern machine learning/AI tools to biological data, target discovery, and regenerative medicine. Ability to work at the intersection of computational data science and translational biology. A strong interest or background in gene regulation, epigenetics, and cell fate transitions to help translate multi-omics patterns into biological mechanism.

Candidature