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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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research focuses on a geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics
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. Research Content: 1. Study the brain network mechanisms of deep brain stimulation neuromodulation. 2. Research on neural biomarkers and closed-loop neuromodulation strategies and methods. 3. Clinical
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& Perception Laboratory (NA&P Lab), led by Dr. Sabine Kastner at the Princeton Neuroscience Institute. The lab studies neural mechanisms of cognition in the primate brain. Intracranial recordings from human
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computational modelling using artificial neural networks. It brings together teams led by Mohamady El-Gaby (Oxford Experimental Psychology), Matthew Nour (Oxford Psychiatry), Rick Adams (UCL), and Maria Eckstein
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regressions, Bayesian analyses). You preferably have experience supervising and/or teaching students. You preferably have knowledge of swarm robotics and/or deep learning artificial neural networks (e.g. CNN
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researcher to investigate the neural mechanisms underlying decision-making, using the fruit fly Drosophila melanogaster as a model system. Funded by the BBSRC, this project will combine innovative behavioural
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fundamental mechanisms of neuronal loss to better understand why neurons die or axons are damaged to ultimately establish new strategies for the preservation or restoration of neural tissue. We use multiple
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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optogenetic investigations of cognition-related hippocampal formation and related brain networks. ? Analysis of electrophysiologically-evoked responses to assess synaptic population changes