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collaborations to implement cosmological analyses derived from the observation of Type Ia Supernovae. This work, carried out within the framework of the ANR SCINF project, aims to develop inference methods based
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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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innovative environment, combining expertise in genomics, epidemiology, and bioinformatics, to develop early surveillance approaches and cutting-edge analytical tools. Where to apply Website https
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-Saclay region and is easily accessible by public transportation from Paris. More information is available at https://www.ip-paris.fr . The candidate will join the doctoral school of the Institut
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, environment and ecology, transportation, robotics, energy, culture, and artificial intelligence. Presentation of CNRS as an employer: https://www.cnrs.fr/en/cnrs Presentation of IRISA as the host laboratory
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accelerators, energy and the environment and health. IJCLab has very significant technical capacities (around 280 IT) in all the major fields required to design, develop / implement the experimental devices
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, they will develop an in vivo approach aimed at manipulating and visualising neuroligin-1. The candidate will carry out electrophysiology and microscopy experiments (expansion, confocal and calcium imaging
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. The work will take place in a hot hyper-arid environment. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5133-DIAALB-002/Default.aspx Requirements Research FieldHistoryEducation LevelPhD
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of physicochemical models to interpret the experimental results. Prepare scientific reports and contribute to peer-reviewed publications. The selected candidate will join the Institut de Physique et Chimie des
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the multidisciplinary PREDIT4FACE project, which was selected within the PEPR Digital Health Call for Projects of France 2030. PREDIT4FACE (PREdictive DIgital Twins for FACial Expression) aims to develop a multiscale