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PhD "In silico prediction of antibiotic resistance" (M/F)

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : mardi 23 août 2022

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General information

Reference : UPR2301-BOGIOR-001
Workplace : GIF SUR YVETTE
Date of publication : Tuesday, August 2, 2022
Scientific Responsible name : Bogdan IORGA
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Recent developments in whole genome sequencing and in machine learning algorithms provide huge opportunities that are largely unexploited in antibiotic resistance, in part due to limited availability of high-quality input data required for training the machine learning models in an unfocused approach. In this work, based on our expertise in antibiotic resistance motifs, we plan to develop an original, knowledge-based approach producing chemistry-aware machine learning models for accurate in silico prediction of antibiotic resistance profiles in clinically-relevant bacteria, using DNA sequencing data. After validation of the models, these predictions may be used in real-time to guide the personalized treatment of patients in clinical settings, as an alternative to phenotypic characterization of antibiotic resistance.

Work Context

The Institute of Chemistry of Natural Substances (ICSN) is a CNRS unit whose research areas range from chemistry to biology. It is part of the University of Paris-Saclay. The ICSN is located on the CNRS campus in Gif-sur-Yvette and has about 125 staff members, including 76 permanent staff members, spread over 3 buildings.
Within the Analytical and Structural Chemistry and Biology department, the student will join the Molecular Modelling and Structural Crystallography team, which consists of 3 permanent staff. He/she will be placed under the hierarchical authority of the team leader. The student will also work in collaboration with Dr Flora Jay (LRI/LISN, Université Paris-Saclay) and with the members of the ANR Seq2diag project.

Constraints and risks

Occupational risks inherent in working with computers.

Additional Information

- Master's degree in chemoinformatics, bioinformatics or data science
- Good knowledge of machine learning and deep learning techniques
- Strong interest in the chemistry-biology interface.

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