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Organisation/Company CNRS Department Laboratoire de physique des interfaces et couches minces Research Field Biological sciences Computer science Mathematics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 17 Jul 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 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

The PhD thesis will be conducted under joint supervision between the Laboratory of Physics of Interfaces and Thin Films (LPICM, UMR 7647) and the Structural Cell Biology laboratory (BIOC, UMR 7654), both located near each other on the École Polytechnique campus. Laurie Calvet (50%, LPICM), a physicist by training and a specialist in computational techniques, will contribute her expertise in physics and electrical engineering. Sébastien Ferreira-Cerca (50%, BIOC) is an expert in ribosome synthesis and function. The collaborator Clément Madru (BIOC) will oversee structural characterization using cryo-electron microscopy (cryo-EM).

Throughout the thesis, weekly meetings are planned between the PhD student and the three supervisors. Additional supervision by each advisor may be provided as needed, depending on the project's requirements.

All necessary equipment is available in the respective research units. Computational work will be supported by the Jean Zay supercomputer. Structural characterization will be carried out with support from CIMEX (Interdisciplinary Center for Electron Microscopy at École Polytechnique). CIMEX is equipped with tools for sample vitrification, as well as a TITAN Themis microscope (300 kV), capable of resolving ribosomal structures to a resolution of 3 Å.

Funding for this project comes from the 2025 “Feuille de Route Santé” doctoral fellowships call.

The objective of this thesis is to develop innovative strategies for antibiotic discovery by combining a graphical model of the ribosome with advanced artificial intelligence techniques to identify structural vulnerabilities, followed by experimental validation using antisense oligonucleotides (ASOs) and cryo-electron microscopy (cryo-EM).
This interdisciplinary project spans data science, AI, microbiology, and structural biology, and integrates both computational modeling and experimental validation.
The first phase (Months 1–6) will focus on constructing a Bayesian model to rank candidate ASO targets. While a database of ribosomal graphical structures is already available, additional biological information—such as nucleotide conservation, involvement in resistance mutations, and antibiotic binding—will be integrated as informative priors within a penalized complexity (PC) Bayesian framework. This approach will yield a ranked list of vulnerable ribosomal sites suitable for ASO targeting.
In the second phase (Months 7–12), the student will transition to the biology lab to gain hands-on experience with ASO screening, in vitro translation assays, and cryo-EM sample preparation. ASO efficacy will be tested using the PURExpress in vitro translation system with GFP expression as a readout. Binding assays with purified ribosomal subunits will be conducted using biochemical and biophysical methods (e.g., co-sedimentation, fluorescence anisotropy). The top-ranked targets from the computational analysis will undergo initial experimental validation.
Subsequent steps will depend on the results of this first round of testing. If promising ASOs are identified, the student will perform more detailed structural analysis using cryo-EM, supported by CIMEX (Centre Interdisciplinaire de Microscopie Électronique de l'École polytechnique), to which the host laboratories have privileged access.
Once the first set of targets has been thoroughly investigated, a second round of target prediction (starting at mont 19), ASO design, and experimental testing will be initiated. In the final year, the student will return to computational work, developing generative models (e.g., variational autoencoders or graph neural networks) to generalize and extend the methodology based on the integrated computational and experimental findings.