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cell tracking to identify the progenitors of these cells during regeneration. • Develop and apply a recombinase-based cell barcoding strategy to trace cell lineages during leg growth and regeneration
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, and lineage-specific dynamics. Assess congruence and robustness of phylogenetic reconstructions using Bayesian inference, parsimony, and tip-dating, and evaluate their impact on macroevolutionary
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, hydro-acoustics, data science, geodynamics, geophysics, statistics, Bayesian inference ⁃ Experience with statistical analyses and machine learning techniques ⁃ Programming in C / python / Julia
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canopy height inference methods accurately represent the variability observed in Central African forests. The research associate will work at the CNRS in Toulouse and will be involved in the activities
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Description You will: * Lead MEG head-cast data collection for a visuomotor reaching/interception study, ensuring robust synchronization with video-based kinematics and eye-tracking, and enforce rigorous
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applications. The project aims to address fundamental theoretical questions related to the representation and measurement of the polarization state, as well as the use of Bayesian and/or statistical learning
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appreciated. - Advanced statistical modeling (GLMMs, state-space models, stochastic Bayesian programming) in R - Experience with bioinformatics, if possible experience in the use of RAD-seq and/or lcWGS data
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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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promotion (oral presentations, articles), • Development of ontologies and inference rules, • Participation in the implementation of validation scenarios in simulated environments. L'équipe « Robotique et
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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