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research project “Is the brain network involved in sentence comprehension replicable and robust?”, led by Dr. Jurriaan Witteman. The project will investigate the neural mechanisms underlying sentence-level
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 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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Conference on Neural Networks (IJCNN), pages 1–10, July 2022. doi: 10.1109/IJCNN55064. 2022.9892277. URL https://ieeexplore.ieee.org/document/9892277/?arnumber= 9892277. G. Bellec, D. Salaj, A. Subramoney, R
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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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within a Research Infrastructure? No Offer Description Work Plan Study and application of methods for extracting understandable concepts and inducing logic-based theories from neural networks. Study of
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staff position within a Research Infrastructure? No Offer Description This research project aims to develop a synthetic dataset generation technique to optimize the training of neural networks (NNs
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staff position within a Research Infrastructure? No Offer Description Title: “Synthetic Dataset Generation Technique to Optimize Neural Network Training for Seismic Data Prediction” Research Area
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Los Alamos has been rated #3 in the Best Counties to Live in the USA. Apply Now https://lanl.jobs/search/jobdetails/sparse-neural-network-design-post-doctoral-research-associates/e80bfca8-271e-4be8-b205
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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 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