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Postdoctoral Fellowship in Meteorology- Development of Radar-based Hail-size Algorithms University of Oklahoma Norman Campus: Atmospheric and Geographic Sciences: School of Meteorology Location
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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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of the elderly to space robotics. We are now looking for a post doc to work on developing quantum computing algorithms. Postdoctoral researcher Are you as excited about quantum computing and its future
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
with a variety of ancillary data are used in a radiative transfer algorithm to estimate soil moisture. This research opportunity involves conducting different analyses to understand the performance
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friendly and international work environment Learn more about CQT at https://www.cqt.sg/ Job Description We are seeking a strong quantum computing researcher to develop quantum algorithms for generative
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state-of-the-art facilities to work on the following: Developing advanced path planning, search, and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments. Designing
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sparse algorithms. The successful candidate will contribute to advancing secure, trustworthy, and efficient AI solutions for scientific applications. Key responsibilities include developing state
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economics Develop state-of-the-art algorithms in algorithmic game theory and fair division Design and conduct experiments to evaluate the algorithm performance Job Requirements: PhD degree in computer science
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference