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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
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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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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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evaluation process etc please visit: ambercofund.eu Minimum requirements • PhD in structural biology/chemistry, with excellent knowledge of biochemistry and molecular biology, including experience in protein
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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/chercheur-fh-en-simulation-des-deformations-des-tis… Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent Research FieldBiological sciences » Biological
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Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
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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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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