M/F Deep Generative Models to Decipher the Dynamics of Cellular Interactions.

CNRS; France; Sur place; EUR 20 000 - 30 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a PhD student focused on developing deep generative models to analyze cellular interactions. The successful candidate will work in a multidisciplinary environment, contributing to model design, validation, and dissemination of research results through scientific publications and conferences. Candidates should have a strong background in deep learning and image analysis, preferably with a relevant Master's degree.

Qualifications

• Experience in deep learning and generative models.
• Familiarity with image analysis techniques.
• Background in biological sciences is advantageous.

Responsabilités

• Develop deep generative models for cellular interaction analysis.
• Design validation protocols for model accuracy.
• Collaborate with team members on scientific publications.

Connaissances

Deep learning

Image analysis

Programming skills

Collaboration with biologists

Formation

Master's degree in a relevant field

Outils

Videomicroscopy data analysis software

Description du poste

Organisation/Company CNRS Department Institut de biologie de l'Ecole Normale Supérieure Research Field Biological sciences Computer science Mathematics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 10 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The project takes place in a multidisciplinary environment combining artificial intelligence, cellular imaging, and biology.

The student will work within IBENS, in a team specialized in image analysis and deep learning.

Deep Generative Models to Decipher the Dynamics of Cellular Interactions.

The PhD student will develop deep generative models to decode and simulate the dynamics of cellular interactions from videomicroscopy data. The main objective will be to design a system capable of generating synthetic video sequences that reflect cellular behaviors under different experimental conditions. This approach will help identify and quantify dynamic differences linked to treatments, cellular states, or specific biological contexts.

The candidate will contribute to model architecture design, validation protocol development, and collaborative experiments with biologists. They will also take part in disseminating the research results through scientific publications and conference presentations.

Activities:

• Design and training of conditional generative models for video sequences.
• Analysis of cell dynamics differences between experimental conditions.
• Validation on biological datasets from tumor models and embryology.
• Contribution to publications and scientific dissemination.