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to modulate physiology and neural activity in the brain, gastrointestinal (GI) tract, and other peripheral organs. These projects have a high potential for translation towards treating a variety of neurologic
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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methods, renormalization group approaches to neural networks, learning-theoretic analysis of algorithms and geometric analysis of the learning landscape. Candidates are encouraged to interpret these subject
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of a PhD; experience in independently teaching university students in the field related to Information and Communication Technology, particularly in the areas of neural networks and their applications
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[Work location] * Address 565-0871 Osaka 1-4 Yamadaoka, Suita City The Center for Information and Neural Networks (CiNet) [Number of hired] Number of hired:1 person(s) Hiring date:2026-04-01 00:00:00
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applied to medical imaging, while leveraging clinically generated data to inform scientific discovery. His previous work has demonstrated the ability of convolutional neural networks to identify systemic
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Job Purpose To make a contribution to an ERC-funded project Dynamic network reconstruction of human perceptual and reward learning via multimodal data fusion, working with Prof. Marios Philiastides
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or associate professor in the following academic areas (or closely related fields): Biology, Chemistry, Economics, Neural Science, Physics, Social Sciences, Global Public Health, Biostatistics, and/or Social