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at Ecole nationale des ponts et chaussées (ENPC), with access to state-of-the-art laboratories, computational resources, and a network of industry collaborators. The project offers opportunities
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of elements between the various departments involved, such as the RS2M department, which will develop a more network-focused platform. Ultimately, the ideal would be to link the various platforms and develop a
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Research FieldPhysics » Applied physicsEducation LevelMaster Degree or equivalent Skills/Qualifications Cryoporometry, NMR relaxation, pore network structure LanguagesENGLISHLevelGood
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 4 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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data from measurement and observation networks, as well as snow testing protocols, to provide access to a larger, harmonized information system on both sides of the border; Mutual training and
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of several researchers working in the field of inverse problems due to their ability of combining variational inference approaches with the ability of neural networks to learn unknown posterior distributions
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optimization of complex systems, intelligent data and information systems, as well as networks, distributed systems, and security. LIMOS stands out for its interdisciplinary approach, combining theoretical
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Information System (GIS): working on and analyzing all the collected data on medieval buildings and their interactions with parcels and the street network, in order to understand their spatial integration
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Networks in order to handle non-linear relationships between covariates and response variables. To this aim, the PhD student will join a consortium of researchers issued from different disciplines with a
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 4 hours ago
. This issue can have safety implications, particularly in closed-loop setups. Physically Informed Machine Learning (PIML), and in particular Physics-Informed Neural Networks (PINN), are less dependent on data