44 machine-learning "https:" "https:" "https:" "https:" "https:" "CNRS " positions at CNRS
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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, understanding and predicting their thermal conductivity from first principles calculations is very challenging. In this doctoral research project, we plan to use machine learning potentials to investigate
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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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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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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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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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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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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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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