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RAP opportunity at National Institute of Standards and Technology NIST Neural Net Deep-learning for Magnetic Resonance Image Reconstruction and Diagnosis Location Physical Measurement Laboratory
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RAP opportunity at National Institute of Standards and Technology NIST Leveraging Artificial Neural Networks for Enhancing GC and LC-MS Metabolomics Data Interpretation and Integration Location
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network (MINDnet)”. The PhD project “Efficient learning algorithms for closed-loop optogenetic control of neural circuits” aims to develop an optoelectronic interface with neural circuits, that optically
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and ensure local relevance. Required
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with
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11.11.2024, Academic staff In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the actual stress of helicopter components using a data-based as well as a physics-based approach. In the project...
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for tokamak fusion reactors. Specific responsibilities include: Neural Surrogate Modeling: Develop, train, and validate fast neural-network surrogate models (e.g., transport surrogates, edge surrogates, free
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identification to characterize neural dynamics, closed-loop network behavior, and state transitions during sleep and seizure events. Real-Time Control & Optimization: Design, implement, and refine real-time
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technology (graph neural networks and transformers) with a dynamical systems framework. The dynamical systems viewpoint imagines the turbulence as a space-time tapestry of connected states, which are related
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with deep-learning, UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum, the deadliest malaria parasite species (read [1] to learn more about the ideas behind