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plateforme de simulation multi-agent implémentée en Pharo - un environnement de programmation dynamique et orienté-objet bien adapté à la construction de systèmes réflexifs et modulaires. La recherche sera
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uncertainties (delays, resources, failures) using various methods, including Bayesian approaches. 3. Optimize the workshop configuration, taking into account scenario variability, by relying on the surrogate
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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) : Aucune Candidatures : CV, lettre de motivation et éventuelles lettres de recommandation à envoyer par e-mail avant le 26/09/2025 à pupillo@unistra.fr , Cc: ibarbara@unistra.fr en indiquant en objet Réf
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associated with phenotypic (biomechanical and metabolomics) traits. Estimate locus-specific effect sizes and quantifying genetically-driven phenotypic variations. Develop Bayesian models and/or deep learning
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features to behavior using GLMMs/Bayesian models; conduct sensitivity and robustness checks. * Method validation: benchmark alternative pipelines (filters, burst detectors, forward/inverse models); perform
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maximum likelihood and Bayesian inference frameworks. - Data mining in genome databases. - Large-scale phylogeny reconstruction (archaea, bacteria, and eukaryotes). - Implementation of complex sequence
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of electromagnetic wave physics or astrophysics, considered an asset. - Experience with advanced statistics and Bayesian inference, which will be regarded as a plus. Familiarity with compressed sensing and the ability
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational