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researcher with expertise in the area of spiking neural networks and an interest in (applications of) probabilistic computing. The postdoc candidate will participate in the NWO NWA project "Acting under
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Summary Research in the Ichinose Lab is in the field of vision and neuroscience. They use the mouse retina as a model system to investigate neural networks and physiological functions and have the
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neuroimaging methods to investigate the cognitive and neural mechanisms of episodic memory and how they change in healthy and pathological aging. Our lab investigates cognitive and neural mechanisms of memory
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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extension. Your Tasks Our overarching goal is to understand the neural mechanisms of cognitive functions, focusing on learning, attention and decision making. We investigate the contribution of subcortical
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of this position will be development of custom neural networks for functional annotation of protein sequences. This is an Extended Temporary Employment (ETE) position. Outstanding UA benefits include health, dental
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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with Dr. Emily Becker and the NOAA Climate Prediction Center. Key responsibilities Train neural networks and quantify uncertainty to evaluate predictability Perform explainable AI (XAI) related research
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 21 days ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and