328 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at CNRS
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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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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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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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python
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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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skills for this position are: o Good knowledge of the technological challenges of agricultural/viticultural robotics. o Proven skills in: vision-based robot modeling and control; computer vision and
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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