(M/F) PhD candidate in reinforcement learning and information theory for embryonic morphogenesis
- FTC PhD student / Offer for thesis
- 36 month
- BAC+5
This offer is available in English version
This offer is open to people with a document recognizing their status as a disabled worker.
Offer at a glance
The Unit
Unité de biologie moléculaire, cellulaire et du développement
Contract Type
FTC PhD student / Offer for thesis
Working hHours
Full Time
Workplace
31062 TOULOUSE
Contract Duration
36 month
Date of Hire
09/09/2026
Remuneration
2300 € gross monthly
Apply Application Deadline : 01 July 2026 23:59
Job Description
Thesis Subject
Early embryonic development transforms a small number of cells into an organized, robust, and reproducible multicellular structure. This PhD project aims to understand how local cellular decisions, based on the cells' internal state, their neighborhood, signaling, and tissue mechanics, can give rise to complex morphogenetic trajectories.
The project will combine physical modeling of tissues, multi-agent reinforcement learning, and information theory. Cells will be represented as agents capable of sensing certain local information and adapting their behavior, such as their division, polarity, adhesion, tension, or signaling. The objective will be to explore which local rules can generate robust embryonic forms, and how to quantify the complexity, information, and reproducibility of developmental trajectories.
The PhD will draw on mechanochemical models and data from complementary embryonic systems, in particular the ascidian, a highly invariant developmental model, and the mouse embryo, which is more variable. The project aims to develop a general computational framework to study the emergence of embryonic form from the interaction between genes, mechanics, signaling, and cellular decisions.
Your Work Environment
The PhD candidate will join the Multiscale Physics of Morphogenesis team, led by Hervé Turlier, at the Centre for Integrative Biology of Toulouse, CNRS UMR5077 / University of Toulouse.
The team develops physical, computational, and machine learning models to understand how embryonic forms emerge from the interplay between cellular mechanics, signaling, gene regulation, and collective decision-making. The project will benefit from a highly interdisciplinary environment, involving interactions with biologists, physicists, applied mathematicians, and artificial intelligence specialists.
The project will build on established experimental collaborations in France and abroad.
The PhD candidate will have access to the team's computing resources, the laboratory's infrastructure, and a network of national and international collaborations. The team is committed to providing a supportive, inclusive working environment that is open to diverse scientific backgrounds.
Constraints and risks
No particular risks have been identified. Full-time position. Remote work may be possible in accordance with the applicable rules and the organization of the project.
Compensation and benefits
Compensation
2300 € gross monthly
Annual leave and RTT
44 jours
Remote Working practice and compensation
Pratique et indemnisation du TT
Transport
Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€
About the offer
| Offer reference | UMR5077-HERTUR-003 |
|---|---|
| CN Section(s) / Research Area | Mathematics and mathematical interactions |
About the CNRS
The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.
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