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) Assessing the performance and fault tolerance of neuromorphic hardware; (b) Designing and developing one or more machine learning (ML) and artificial intelligence (AI) algorithms to support and enhance
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, including but not limited to: o Quantum error correction, fault tolerance, and resource optimization. o Entanglement dynamics, quantum control protocols, and hybrid quantum-classical algorithms. o Modeling
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of the occurrence of rare events, as well as model simplifications and associated a posteriori error estimate. This will mainly rely on the construction of optimal control strategies associated to the large deviation
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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
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month 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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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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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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applying only the amounts needed at the right time, thus avoiding nutrient leaching, soil degradation and, above all, water wastage. In contrast to in situ measurements, remote sensing data provide frequent