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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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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The objective of this postdoctoral position, in
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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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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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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, 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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of London Cancer Institute. Objectives: The aim of the project is to study chromatin remodeling defects in cancer. The successful candidate will be responsible for designing and executing experiments
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(fMRI), and eye-tracking measurements (main task) – Data analysis (main task) – Interpretation of results (main task) – Dissemination of findings through the writing of scientific articles (main task