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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 15 days ago
(graph neural networks and transformers) The objective is to learn conformational heterogeneity directly from molecular dynamics simulations and to identify and predict allosteric communication pathways
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photonic systems, in particular, make it possible to harness the richness of optical dynamics to perform complex operations inspired by biological neural networks. However, current approaches face
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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the design of AI for mobile network operation [1-12], which represents an ideal foundation for the student to make meaningful contributions to the field. Where to apply Website https://networks.imdea.org/job
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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will build on research that detects different types of uncertainty in deep neural networks, and we will connect this uncertainty to interactive data collection, e.g. in the form of a dialogue with the
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for quantized and pruned neural networks, creation of quantized and pruned demonstration models, reproduction of state of the art, experiments in heterogeneous quantization Depending on expertise
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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when the shared representations between tasks are limited or trained. This project aims to test these predictions using a behavioral, neural and real-life approach. We will focus on young adults, but
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software