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the BENEFIT project, funded by the French National Research Agency (ANR), to work on active flow control and machine learning. 1- CONTEXT -------------------- Active flow control aims to modify velocity fields
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opportunities for machine learning to address outstanding biological questions. The PhD (M/F), to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development of a new method
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stochastic modeling, Bayesian inference, data fusion and modern machine learning. Its research activities span various application domains such as security, non-destructive testing, infrared imaging and
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Information Eligibility criteria Scientific and technical skills: • PhD in applied physics, optics, astrophysics, control systems, or artificial intelligence. • Strong background in machine learning
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Differential Imaging (CDI) to exploit the fundamental property of light coherence. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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time scales. To do this, we will build on a landscape picture of stochastic gene expression dynamics inferred from data using modern machine learning techniques. The results will inform us about how
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LanguagesFRENCHLevelBasic Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria Degree : PhD in computer science, machine learning, or computational biology We expect a
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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MICADO (the first light instrument of the Extremely Large Telescope). The project provides a collaborative network, engaging with leading experts in optics, astrophysics, and machine learning from