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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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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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: The ideal candidate should have: * Knowledge of machine learning, especially neural networks or graph neural network or federated learning. * Strong mathematical and algorithmic background (optimization
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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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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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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