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, applying deep domain knowledge and advanced quantitative methods to inform critical development decisions. At Northeastern University, the Fellows will engage in scientific publication, conference
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Electronique, Energie, Automatique (EEA) ou équivalent. Le candidat doit posséder un bon niveau en mathématique et des connaissances en traitement du signal. Des connaissances en machine learning/deep learning
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, we believe science can achieve its fullest potential. THE ROLE During your internship you will work on a projectin the Cultural Heritage Technologies (https://www.iit.it/it/web/cultural-heritage
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
more advanced concepts such as tools for supervised/ unsupervised learning that will be helpful for deep learning focused courses. Estimated course enrolment: 35 Estimated TA support: 1 Class schedule
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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learning. A record of contributing to building and maintaining effective and productive links locally and nationally with the discipline, profession and wider community. Tasmanian Working with Vulnerable
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(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
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experience in machine learning and artificial intelligence who can teach in areas relevant to AI including machine learning, deep learning, natural language processing, reinforcement learning, robotics and
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling