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at EM2C lab, which consists in introducing virtual species and reactions whose thermodynamic and chemical properties are optimized by machine learning algorithms to retrieve properties of reference flames
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French National Institute for Agriculture, Food, and Environment (INRAE), Jouy-en-Josas | France | 27 days ago
developed by MaIAGE partners (e.g. Omnicrobe application*, Ontobiotope ontology*, extraction workflow). Expected results:The PhD student will design and evaluate original machine-learning-based methods
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concepts such as distributed cognition, edge computing, machine learning, formal ontologies, unplugged artificial intelligence, frugal computing... Smart Villages (a rural version of the Smart City) and
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optimize the training and inference of modern deep learning architectures. Potential applications will include, but not be limited to, computer vision, natural language processing, climate, etc. References
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cluster computer system to realize big-data analysis and simulations. Mission confiée Context of the project Artificial Intelligence (AI) and especially Deep Learning (DL) have undergone many successes in
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models, semi-supervised learning, surface reconstruction from images, photogrammetry for VR.Summary:The 3D reconstruction of a complete environment from images is useful in a lot of applications including
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materials using online sensoring, machine learning and digital decision-making tools This PhD offer (PhD 13) is part of the 15 PhD contract proposals related to the European CESAREF project (www.cesaref.eu
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of the thesis is to design a methodology implemented in a tool that takes low-level RTL models as input and automatically generates an ISS by exploiting recent advances in machine learning (ML) such as Graph
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design, and innovative computer architectures. Our research interests range from the study of fundamental phenomena to the design of new devices with potential for technological applications in information
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. The objective of this work is therefore to accelerate the convergence of preconditioned linear system resolution through Machine Learning. Two main approaches will be investigated especially for the coarse