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access to the University's research resources, working under the mentorship and guidance of Associate Professor Courtney Fung, PhD, and Honorary Professor Bates Gill, PhD. The fellowship is structured
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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deep learning approaches, with a particular interest in developing methods capable of handling scarce or corrupted data, designing methods for specific imaging modalities, or understanding and
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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analog, requiring the model to integrate multimodal inputs to anticipate the onset and spatial evolution of ionospheric storms. The successful candidate will work at the intersection of deep learning and
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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field (including Mathematics Education) with 18 graduate credit hours in Mathematics, Applied Mathematics, or Statistics PhD preferred Minimum Experience/Training: Prior college teaching experience is
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learning and deep learning. Specific Requirements PhD in acoustics. Knowledge of acoustical measurement techniques, as well as physical acoustics. Some experience in acoustic signal processing and machine