PhD (M/F) in machine learning for RNA-RNA interactions
New
- FTC PhD student / Offer for thesis
- 36 months
- 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
Laboratoire de Biologie Structurale de la Cellule
Contract Type
FTC PhD student / Offer for thesis
Working hHours
Full Time
Workplace
91128 PALAISEAU
Contract Duration
36 months
Date of Hire
01/10/2026
Remuneration
2300 € gross monthly
Apply Application Deadline : 06 July 2026 23:59
Job Description
Thesis Subject
**Project Overview**
Segmented RNA viruses, such as influenza A virus, evolve in part through genetic reassortment, a process that generates novel viral combinations with potential pandemic consequences. Although an enormous number of segment combinations are theoretically possible, only a subset is observed in nature, suggesting the existence of compatibility constraints between RNA segments. This PhD project aims to develop hybrid models combining statistical learning and physical modeling to identify and predict the RNA–RNA interaction networks that constrain viral reassortment. The work will integrate the analysis of large viral sequence databases, high-throughput measurements on individual viral particles, and RNA structural mapping approaches within a tightly integrated experimental–computational framework.
**Research Objectives**
The PhD candidate will develop probabilistic and generative models, such as Direct Coupling Analysis (DCA) and Variational Autoencoders (VAEs), trained on large viral sequence datasets. The project will also incorporate physical constraints related to the energetics of RNA interactions into statistical learning frameworks. Model predictions will be quantitatively compared with experimental data obtained through high-throughput approaches, following an iterative "learn–design–test" strategy for model refinement. The overall goal is to build predictive models of viral genome compatibility capable of simulating reassortment scenarios involving emerging viral strains.
**Collaborations**
The project is embedded within a national interdisciplinary consortium and will involve close collaborations with the **Institut Pasteur** for influenza virology and reverse genetics, with **CNRS** teams specializing in nucleotide-resolution RNA structural analysis, and with microfluidics and single-viral-particle sequencing platforms.
Your Work Environment
We are seeking a candidate with a background in physics, applied mathematics, computational biology, or artificial intelligence, with strong Python programming skills, a keen interest in probabilistic modeling and generative AI, and a strong motivation to work at the interface between theory and experimentation in the fields of RNA biology and viral evolution.
Constraints and risks
NA
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 | UMR7654-PHINGH-001 |
|---|---|
| CN Section(s) / Research Area | Molecular and structural biology, biochemistry |
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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