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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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aging tests and electrical measurements to assess component reliability. -Ability to analyze and interpret experimental data (physical signatures, correlations with reliability). -Use of artificial
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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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of 9 people : 4PhD students, 3 post doc, 1 research engineer and 1 assistant. The team is associated with the Institut Pasteur's Computational Biology Department, UMR3738 and the PRAIRIE Artificial
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel
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to work on the use of hyperspectral data to explain and predict soil functions and communities in European mountains. We are looking for a candidate who has a very good command of artificial intelligence
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is an interdisciplinary effort at the frontier between Biology (Genetics, Genomics), Bioinformatics, Artificial Intelligence (Neural Networks) and Statistics (LMMs). The aim is to join the