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/acsphotonics.3c01104). This method is based on the combination of a color two-photon excitation method developed at the LOB, automated serial sectioning, and the 'brainbow' approach developed at the IdV allowing
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production provides a competitive advantage to commensal bacteria, thereby limiting the establishment of pathogenic strains. This work adopts a multidisciplinary approach, combining methods from molecular
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of different natures (gradient-based optimization, sampling-based methods, and learned policies) within a single coordination scheme. This involves assigning each subproblem to the most suitable approach and
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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Saudi students in the methods and techniques used in archaeobotanical analysis. Logistics - Contribute to the preparation of field missions, in collaboration with the director and project manager
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been proposed: traditional computer‑algebra methods [3], reduction of the problem modulo a prime p [6, 2], and symbolic‑numeric methods [4, 1]. This postdoc proposal concerns the second modular approach
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this will be addressed using advanced NMR methods. and integrative modelling, providing real- insights into conformational changes governing CB1R selectivity. This project offers the opportunity
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fluorescent labeling tools and automated particle-tracking methods, to directly measure membrane fluxes during development. These approaches will enable the quantification, at the level of intracellular