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projects that apply machine learning and advanced computational modeling to integrate multi-omics, clinical, and imaging data for biomarker discovery and mechanistic insights in AD. The position offers
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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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: What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)? What are appropriate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
the development and implementation of machine learning models Special Physical/Mental Requirements Special Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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supermassive black holes through a combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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collaboration with colleagues in the John Radcliffe Hospital and the Oxford Big Data Institute, with the central aim being the development of rapid diagnostics of antimicrobial resistance in clinical samples. You