ingénieur d'études en bioinformatique structurale

 CDD · IE  · 12 mois    Bac+5 / Master   Génie Enzymatique et Cellulaire, CNRS UMR 7025 · Compiègne (France)

 Date de prise de poste : 2 février 2026

Mots-Clés

machine learning stochastical modeling molecular modeling single-stranded oligonucleotides

Description

JOB DESCRIPTION
Single-straded oligonucleotides (ssNAs), a powerful biotechnological tool, are capable of selectively and specifically recognizing their molecular targets thanks to the 3D structures they can adopt. Therefore, the design of ssNAs for diagnostics and therapeutics applications needs the definition of the ensemble of the ssNAs sequences folding into a desired structure (inverse folding). Different numerical tools for ssNAs inverse folding exist, but they provide a limited number of results, without the association of a quality measure of the provided sequence and they focus only on the ssNAs secondary structure. We aim at providing a full and transferable procedure combining numerical and computational methods to experimental validations to design ssNA sequences having a desired structure and function. The first application will focus on the design of ssNAs for a direct diagnosis of Lyme disease.

Activities description

Position: Ingénieur d’Etudes (research engineer)
Principal mission: Research activity on the development of numerical workflow for single-stranded oligonucleotides 3D inverse folding
Missions
* Bibliographical research
* In silico molecular modeling
* Stochastic modeling

Activities linked to the missions
* Usage of molecular modeling software
* in silico protocols optimization
* Coding

Contract information

Location: EA 2222 “Laboratoire de Mathematiques Appliquées de Compiègne) unit and CNRS UMR 7025 « Génie Enzymatique et Cellulaire » unit (UTC)
Supervisors: Pr. Ghislaine Gayraud, Dr Miraine Dávila Felipe and Dr Irene Maffucci
Working time: Full time
Duration** : 12 months

Required competences
* Stochastic modeling/ Machine Learning and/or in silico molecular modeling (molecular dynamics, docking, affinity predictions)
*English (minimum B2 level)

Technical skills

  • Machine learning models
  • Usage of molecular modeling software
  • Programming skills

Soft skills
* Independence, rigor, sens of initiative, Structured work approach, Task prioritization and planning
* Communication skills
* Work team

Candidature

Procédure : Pour candidater, envoyer un mail à irene.maffucci@utc.fr, ghislaine.gayraud@utc.fr et miraine.davila-felipe@utc.fr avec un CV, une lettre de motivation et un ou deux contacts de référence.

Date limite : 31 mars 2026

Contacts

 Irene Maffucci
 irNOSPAMene.maffucci@utc.fr

 Ghislaine Gayraud
 ghNOSPAMislaine.gayraud@utc.fr

Offre publiée le 2 décembre 2025, affichage jusqu'au 31 mars 2026