Mots-Clés

Omics data AI models pharmaco-genomics pharmaco-epigenomics predictive pharmacology

Description

Offer: Master 2 internship - AI models in pharmacogenomics (CEA Grenoble)
Title: AI models in pharmacogenomics
Background: Translational pharmacology is a discipline seeking to move results from molecular pharmacological research to the clinic in order to optimize the choice of drugs, their dosage and safety in patients. Specifically, it seeks to translate knowledge acquired from molecular and preclinical models to the bedside patient studies. Within the multidisciplinary research consortium DIGPHAT (Digital Pharmacological Twins), funded by the Digital Health research program of the France Relance 2030 plan, we seek to develop a global approach in systems pharmacology to better personalize drug therapies. We will achieve this by developing interoperable pharmacological models integrating multi scale and longitudinal health data.
Objective: The intern will be involved in bioinformatics approaches for the preparation of multi-scale cohort data and in the development of AI models in pharmaco-genomics (PG) and pharmaco-epigenomics (PeG).
Workplan: The intern will participate in the collection of omics and health data as well as their normalization and the generation of high-level biological features using bioinformatics methods. He/she will then work on the prototyping of general AI models in PG and PeG applicable to the different pharmacological use cases investigated by the DIGPHAT consortium, namely immunosuppression for transplantation, antibiotic resistance and precision oncology. He/she will finally initiate work to mature a predictive model by taking advantage of multi-scale data specifically available for one use case.
Host laboratory: The intern will be hosted in the MODAL (Multiscale bioinformatics & digital health) group (https://www.bge-lab.fr/en/GenChem/Research/MODAL) within the “Genetics & Chemogenomics” team of the Interdisciplinary Research Institute of Grenoble (CEA). He/she will be supervised by Christophe Battail, expert in computational analysis of omics data, and will evolve in a multidisciplinary research environment with bioinformaticians and biologists. The intern will strongly interact with engineers, researchers and clinical pharmacologists from the DIGPHAT consortium.
Candidate profile:
Knowledge and skills in bioinformatics: Omics data, machine learning and biostatistics methods, unix command line and Python/R programming.
Professional aptitude: curiosity and desire to improve their scientific and technological skills, rigor and organization, and
ability to work in a team and interact with other students, engineers and researchers
Job contract:
6-month master's internship starting in February 2024.
Please send a CV, a cover letter and the name of a referee to christophe.battail@cea.fr.