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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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, the recruited researcher will contribute to the development of a novel time-resolved fluorescence lifetime measurement approach, in close connection with methods based on single-molecule brightness analysis
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, resilience and evolution of marine life to develop solid theories and predictive models of the relationships between marine biodiversity and ecosystem functions, which will in turn lead to improved economic
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that of climate disruption, which calls for a systemic transformation of the economy toward sustainable development. This ecological transition relies on the commitment and behavioral changes of socio
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, as well as to the development of a structured database generated from ab initio calculations and enriched through machine learning approaches. The objective is to develop predictive tools to analyze
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of understanding cirrus clouds in climate studies. We are seeking postdoctoral researchers to contribute to our mission of advancing knowledge on how aerosols influence cirrus formation and their evolution, under
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gestures and multimodal speech. - Familiarity with multimodal speech processing software (ELAN, Praat, Anvil, or equivalent): development of comprehensive annotation templates/schemes (hierarchical
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of individual ice crystals and grain boundaries. This postdoctoral project aims to leverage these developments to better constrain the origin, structure, and evolution of basal ice beneath the Greenland and
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7130 CNRS/Collège de France/EHESS/EPHE) and the Centre d'Ecologie Fonctionnelle et Evolutive (UMR 5175 CNRS/Université Montpellier/IRD/EPHE), partners in the ANR ECOPATHS project on the ecology
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling