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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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Intelligence, or a closely related discipline. Strong research background in AI and machine learning, with a focus on efficient or accelerated models. Proven experience with model compression techniques, such as
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Polytechnic University (UM6P), Benguerir, Morocco, is seeking for a postdoctoral candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related
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growing and supportive team with internationally recognized expertise in data management and machine learning. The group has a strong network of national and international collaborators in both academia and
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 25 days ago
). Revisiting Ionosphere-Thermosphere Responses to Solar Wind Driving in Superstorms of November 2003 and 2004. J. Geophys. Res., 122. https://doi.org/10.1002/2017JA024542 . 2. McGranaghan, R. M., A. J. Mannucci