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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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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
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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
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes