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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 28 days ago
of the variability of physiological and cell division processes. The development of the BRP framework will entail modelling, analysis, and inference, and will exploit microfluidics experiments comprising single-cell
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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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of replicable quantitative methods to infer their role in past cultural systems would allow a thorough documentation of their evolution in relation to other aspects of Pleistocene material culture. The objective
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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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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 9 days ago
training a classification model to label a given proposal lesion as positive or negative depending on several characteristics of the individual inferences from each of the different available acquisitions