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Université Paris-Saclay GS Sciences de l'ingénierie et des systèmes | Saint Aubin Routot, Haute Normandie | France | about 10 hours ago
matrix that will be patient specific. The stress maps in 3D will be projected in 2D images. A Neural Network approach for image recognition will be implemented to compare and classify the stress 3D maps
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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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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humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. It is probably fair to say that an artificial neural network can perform better than a human in any
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mechanics) with data-driven techniques. • Proficiency in Python (NumPy, TensorFlow/PyTorch) and MATLAB. • Familiarity with deep learning architectures, particularly recurrent neural networks (e.g., LSTM) and
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involve the adoption of various neural network architectures, including Convolutional, Artificial, and Spiking Neural Networks and their embedding into electronic platforms such as ARM-CORTEX, RISC-V and
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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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of research include diagrammatic calculations, quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and artificial intelligence and neural networks, with a particular
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, transformers, graph neural networks, fine-tuning techniques, and inference and optimization. Writing academic reports Delivering high-quality work at a fast pace, understanding the importance of time and speed
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), Multilayer Perceptron (MLP), Autoencoders, Convolutional Neural Networks (CNNs), and Kolmogorov–Arnold Networks (KANs). Desirable knowledge of Gradient Boosting models such as HistGBM, LightGBM, and XGBoost