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to architectures, algorithms, and applications. Titled "Exploring Atomic Ensemble Qubits for Distributed Quantum Computing," this LBNL-funded initiative brings together experts from the ESnet, NERSC, and AMCR
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acoustic propagation that are applicable to a wide range of ocean environments. (40%) Lead efforts to translate propagation and uncertainty models into computationally efficient algorithms that can be
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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was created to advance research in the mathematical, algorithmic, and statistical foundations of data science and their application to other disciplines. In addition to providing support to core foundational
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(CAMERA) has a new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department of Energy (DOE) experimental facilities. This role involves
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
to the development and validation of computational methods that integrate images with other multimodal data. The postdoctoral research associate will design, implement, and evaluate algorithms and models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
to the development and validation of computational methods that integrate images with other multimodal data. The postdoctoral research associate will design, implement, and evaluate algorithms and models
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. Develop algorithms to identify specific seabed characteristics. Conduct independent research related to underwater active acoustics and seabed sensing. Collaborate with researchers from academia, industry
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machine learning techniques to raw ultrasound data. The project involves developing novel algorithms that integrate physics, engineering, and AI to extract meaningful and clinically relevant information
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, and development of algorithms for real-world clinical data. The fellow will have the opportunity to work with clinicians, engineers, data scientists, trainees, and research staff in a highly