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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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neural network models, produce stimuli for artificial and biological agents, participate in experiments with chicks maintained in the Biological Services Unit, contribute to lab meetings and research
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statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as
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neural network models, produce stimuli for artificial and biological agents, participate in experiments with chicks maintained in the Biological Services Unit, contribute to lab meetings and research
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Biology and Systems Neuroscience to study how sleep deficiencies may impact neural development and transcriptomics in rodent models. The selected candidate will be hired at the University of Oslo and join a
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geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants
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–Newark (https://sasn.rutgers.edu/center-molecular-and-behavioral-neuroscience ). Applicants would join the Benasich lab (https://www.babylab.rutgers.edu ) that conducts studies on the early neural
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self
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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and