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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 12 hours ago
error modeling and encounter clustering (Sentry) Field of Science: Planetary Science Advisors: Steven Chesley steve.chesley@jpl.nasa.gov (818) 354-9615 Applications with citizens from Designated Countries
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: - Quantum computing with qudits, quantum error correction and fault-tolerance - Quantum optics of trapped ions and Rydberg atom arrays - Numerical tensor network techniques - Topological order and (de
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Europe. Your role will be to contribute to the development new techniques for trapping and manipulating atoms, as well as studies of systematic errors in tweezer array atomic clocks. The successful
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on natural language processing, computational linguistics, and data science. The main lab research areas are Arabic natural language processing (orthography normalization, grammatical error correction
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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languages such as Python or C Solid background in quantum mechanics Experience from working on quantum error-correction, open quantum systems, quantum optics, bosonic codes or continuous-variable quantum
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mechanisms of inborn errors of immunity. Job Description Primary Duties & Responsibilities: For more information on the lab please visit: https://megancooperlab.wustl.edu/ . Information on being a postdoc at
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disadvantages in the application due to false degrees, research achievements, errors, and incomplete documents. We are not accepting applications for this job through AcademicJobsOnline.Org right now. Please see
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are not limited to superconducting quantum circuits, circuit QED, quantum error correction, microwave quantum optics, variational quantum algorithms, and the application of machine learning to quantum
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757