371 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at CNRS
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Deadline 7 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As part of this postdoctoral research, capillary debinding will be
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4 Oct 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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mechanisms engaged when deciding to transmit information in social networks. Candidates must have (or nearing completion of) a PhD degree in neuroscience, social psychology, behavioral economics, computational
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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vision researchers to design algorithms specifically tailored for the extraction and analysis of these historical diagrams. EIDA considers these diagrams both as visual heritage and as tools
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Eligibility criteria Instrumental optics and imaging (microscopy, camera detection) for biology. Skills in coding and experiment control. Basics of machine learning and/or signal processing. Teamwork
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Centre International de
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-EHESS, located in Paris in the 6th arrondissement. CAMS is a multidisciplinary research unit bringing together mathematicians, physicists, computer scientists and researchers in cognitive and social
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify