PhD on AI models in pharmacogenomics (CEA Grenoble)

PhD on AI models in pharmacogenomics (CEA Grenoble)Description Offer : PhD on AI models in pharmacogenomics (CEA Grenoble) 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. Specif ically, 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 o f 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 PhD student will implement bioinformatics and AI approaches for the generation of features from multi-scale (molecular, cellular, clinical) cancer data of patient cohorts, and develop AI models in pharmaco-genomics (PG) and pharmaco-epigenomics (PeG). Workplan : The PhD student will participate in the harmonization of omics and health data as well as the generation of high-level biological features using bioinformatics and AI methods. He/she will then develop and evaluate 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, with a focus on this last use case. Host laboratory : The PhD 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 PhD student will strongly interact with engineers, researchers and clinical pharmacologists from the DIGPHAT consortium. Candidate profile : Knowledge and skills in bioinformatics : Omics data, machine learning & biostatistics, unix, 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 : 3-years PhD contract starting in October 2024. Procédure : Please send a CV, a cover letter and the name of two referees to [email protected] Battail #J-18808-Ljbffr