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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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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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platforms, as well as extensive networking opportunities within the University of Miami’s robust AI and digital health ecosystem. Program Objectives: Provide fundamental training by interdisciplinary faculty
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spared from injury into personalized musculoskeletal models to enable robust neural control of robotic assistance in stroke survivors. Real-time characterization of the effect that electrical stimulation
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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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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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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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contributes advanced tools for spatial molecular phenotyping, including smFISH-based spatial transcriptomics, spatial validation of single-cell data, and gene regulatory network mapping during neural
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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knowledge about recent neural network architectures for machine learning (e.g., CNNs, RNNs, GANs) have considerable experience with a deep learning framework are curious about the cross-field between signal