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graphs, check the correctness of AI-generated structures, and even guide neural networks during inference. By combining techniques from grammatical inference, reinforcement learning, and efficient search
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trustworthiness of mathematical models and machine learning tools (e.g., neural networks) in a meaningful way, we need innovative, scalable methodologies that efficiently and accurately capture, represent, and
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., neural networks) in a meaningful way, we need innovative, scalable methodologies that efficiently and accurately capture, represent, and reason about uncertainties within principled frameworks. You will
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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neuromorphic circuits. We will also simulate high-efficiency spiking neural networks (SNN) and build neuromorphic sensory systems to validate performance and explore broad biomedical and other potential
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas dynamics in urban environments. Gas dynamics shape air