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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 approaches, but focus
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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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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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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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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 that focus
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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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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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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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. Job Description: A postdoctoral position is available in the laboratory of Catherine Marcinkiewcz, Ph.D. at the University of Florida. Research in the Marcinkiewcz lab focuses on unraveling neural
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We