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Field
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probably fair to say that an artificial neural network can perform better than a human in any environment it has complete knowledge of. These developments however impose growing demand on our computing
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(posted 2025/10/06 05:00 AM) Position Description: Apply Position Description Sparse Neural Network Design Postdoctoral Research Associate Los Alamos National Laboratory Los Alamos, New Mexico Los Alamos
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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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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize
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. The project focuses on high-precision optical computing for neural network applications and leverages a radically new digitized optical computing architecture. You will play a central role in advancing
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
complexes. The successful candidate will develop novel graph neural network (GNN) architectures to learn dynamic information from molecular dynamics (MD) simulations of protein-protein and protein-nucleic
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research on memory and behavior is how the brain responds to environmental stimuli, and a major challenge here is the heterogeneity of cell-signaling pathways, brain cells and neural networks. We study
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fundamental questions about the transcriptional regulation of inhibitory neuronal development and function in neural circuits, the role of cerebellar circuit dysfunction, and disrupted gene regulatory networks
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model