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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and
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neural network models, produce stimuli for artificial and biological agents, participate in experiments with chicks maintained in the Biological Services Unit, contribute to lab meetings and research
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self
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approximation theory can be automated by a (neural network) guided search over the action space of standard tools (e.g., Hölder inequalities, Sobolev embeddings, ...). Certain proofs in these fields require
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project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely
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project funded by the Waterloo Foundation , exploring the neural mechanisms of balance control in children with and without Developmental Coordination Disorder (DCD). This is a hands-on role that will
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project funded by the Waterloo Foundation , exploring the neural mechanisms of balance control in children with and without Developmental Coordination Disorder (DCD). This is a hands-on role that will
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain