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Field
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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at the University of Arizona is seeking a postdoctoral research associate to develop theoretical and computational approaches for understanding how biological neural networks organize activity through nonlinear
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, thalamus and cortex. Neural recordings are acquired across the basal ganglia thalamo-cortical circuit to examine changes in cell population dynamics, oscillatory activity, and connectivity as motor signs
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on the mathematical theory of deep learning as part of the joint NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
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animal electrophysiology. Familiarity with epilepsy, seizure dynamics, ictogenesis, neuromodulation, or neural network analysis. Experience with small-animal stereotactic surgery, viral-vector
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo
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, from genes and molecules to brain networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139, part-time / full-time, in accordance with the project funding contract and the activity implementation
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for tokamak fusion reactors. Specific responsibilities include: Neural Surrogate Modeling: Develop, train, and validate fast neural-network surrogate models (e.g., transport surrogates, edge surrogates, free
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computations emerge from cortex-wide neural dynamics across species. The PDRA will contribute primarily to developing and analysing Cortically-Embedded Recurrent Neural Networks (CERNNs) that simulate large