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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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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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(posted 2025/10/06 05:00 AM UnitedKingdomTime) Position Description: Apply Position Description Sparse Neural Network Design Postdoctoral Research Associate Los Alamos National Laboratory Los Alamos, New
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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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. 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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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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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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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE
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effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate
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contribute primarily to developing and analysing Cortically-Embedded Recurrent Neural Networks (CERNNs) that simulate large-scale neural dynamics during cognitive tasks. These models integrate species-specific