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systems. The position offers an exciting opportunity to engage in cutting-edge research that addresses both theoretical challenges and practical applications. Key Responsibilities: Develop innovative
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 1 month ago
climate system and the effect of ice-atmosphere feedbacks (e.g., ice albedo feedback and cloud feedback) on sea ice evolution. Developing radiative transfer algorithms to improve both the physical
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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The opportunity The University of Liverpool is a key partner in a £14 million initiative (https://tinyurl.com/yc5z768m) to develop a sustainable, next-generation manufacturing facility, using
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that accounts for ion pairing in a consistent manner Apply the developed equation of state for different applications, including liquid-liquid equilibria, where a robust and sophisticated algorithm may need to be
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to support a two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and
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for mobile communications and radar systems. We are looking for motivated postdoctoral research candidates to participate in the development of signal processing algorithms for multi-antenna integrated sensing
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources