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learning to reconstruct perceived information from functional MRI in real time. For more information on Professor Norman’s lab, see https://compmem.princeton.edu. Questions should be directed to Professor
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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
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: natural language learning, large language models, foundation models, transformer models Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications
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of artificial intelligence and scientific computing including physics-informed neural networks and digital twins; uncertainty quantification, high-dimensional data analysis, and visualization; generative models
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skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly motivated, ability to identify potential problems and
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about neural network behavior from first principles. The role also requires knowledge of microscopy data formats and tools such as Zarr and Neuroglancer. We seek candidates who can think critically about
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project, “Material Model Discovery from Physics-Encoded Neural Networks”, aims to discover material models that give unprecedented accuracy and computational efficiency, enabling engineers to fully exploit
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for biomolecule design and characterization Improving reliability and generalization of deep neural networks Neural rendering for real-time organ reconstruction Surgical video analysis Internet Technology and
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Infrastructure? No Offer Description Electroencephalography (EEG) and magnetic resonance imaging (MRI) can detect early alterations in neural networks that manifest as cognitive and sleep-wake cycle disorders in
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A Research Specialist position is available in the Princeton Neuroscience Institute at Princeton University. The Falkner lab (https://www.falknerlab.com/) focuses on neural circuit dynamics in