Job offer

UNIVERSITE COTE D'AZUR
2 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Tech stack

Python
Machine Learning
Molecular Modelling
Information Technology
Programming Languages

Requirements

Master Degree or equivalent

Skills/Qualifications

§you possess a master degree in computational chemistry, bioinformatics, or a related field

§you have mastered molecular modeling techniques, machine learning algorithms, and programming languages like Python

§you are highly collaborative, with excellent communication skills and a passion for interdisciplinary research

§you like tackling complex problems, working at the interface of biology, chemistry, and computer science, and publishing your results in top-tier journals Internal Application form(s) needed 2026-ICN06 English version.pdf English (489.13 KB - PDF)

About the company

Join us at Université Côte d'Azur, recognized since 2016 for its scientific and educational excellence, to create a responsible and innovative university to serve as a model for the 21st century. Within ICN, the ChemSenSim group (https://lab.chemsensim.fr/) develops interdisciplinary research projects on the molecular basis of chemical senses (smell and taste). Understanding the chemical ecology of an animal requires in-depth knowledge of the odors it perceives and their effects. Using computational approaches, this project aims to decode the function of insect odorant receptors. By doing so, we can identify new semiochemical compounds that manipulate insect behavior and develop more effective biocontrol methods This PhD project aims to elucidate the molecular mechanisms underlying pheromone detection by odorant receptors, which belong to the ion channel family, and identify new active compounds using molecular simulations. Two complementary strategies will be employed: structure-based virtual screening (docking simulations + molecular dynamics) and ligand-based virtual screening (machine learning models). We have recently demonstrated the feasibility of identifying new bioactive molecules via ML techniques [1,2] and structure-based approaches [3] and that MD simulations can capture receptor dynamics relevant to binding event [4]. Predicted ligands will be evaluated experimentally by research partners (iEES Versailles, IBS Grenoble).

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