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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
optimization algorithms Extensive communications protocol design Solid understanding of physical/link layer communications, including synchronization, beamforming, and autonomous rate adaptation Point of Contact
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decoherence dynamics and other effects of quantum-classical correlations in complex fluid solvents. The methodology will be largely based on Lie group methods and structure-preserving algorithms in geometric
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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mathematical fundamentals and algorithm skills or experience. Excellent programming, debugging, performance analysis, and test design skills. Ability to work independently. Excellent communication and
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Bioinformatics and Computational Biology headed by Ivo Hofacker. Our team works on the development of algorithms and methods for problems in Computational Chemistry, Systems Chemistry, and Computational Biology
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the capabilities of fully digital Large Intelligent Surfaces. Subject description The research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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efficient algorithms with provable statistical guarantees, using tools from: high-dimensional statistics, optimization, probability theory, etc. These positions would be especially relevant for those with a
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advantage may arise. The focus of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances