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, and current AI models diverge from them due to superhuman and unrealistic memory capacities. This 3-year PhD project aims to incorporate biological and cognitive constraints into neural network
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in the Department of Psychiatry ( https://seolab.yale.edu/ (Link is external) ). The research focus of the laboratory is to understand how the brain regulates cognition and motivation via large-scale
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recurrent neural dynamics, with particular emphasis on how inhibition and feedback constrain reachable network states and transitions. Analyze large-scale electrophysiological and behavioral datasets
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complex, structured data, with a focus on graphs. It will investigate methodological improvements to Graph Neural Networks (or related models), based on Network Science and structural properties of the
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Your Job Power grids are naturally represented as graphs, where buses, lines, transformers, generators, and loads interact through physical constraints. Recent developments in graph neural networks
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inference with deep-learning, UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum, the deadliest malaria parasite species (read [1] to learn more about the ideas
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opportunity to work on cutting-edge research in neuroimaging, computational neuroscience, and precision therapeutics for aphasia recovery. Key Responsibilities Neural Network Engagement & Language
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techniques, including classical molecular simulations at different levels of resolution and the development and application of neural network models, including generative modeling approaches. Candidates
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highly motivated Postdoctoral researcher (W/M) to join our Neuroengineering Laboratory at EPFL ( https://go.epfl.ch/ramdya/ ) to work on an ERC funded project aiming to reverse-engineer insect limb motor
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neural network types for efficient hardware implementation Developing an FPGA-optimized design Performing device-aware training using both simulated and measured datasets Validation of the complete