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Field
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Responsibilities Conduct research in computational methods for environmental and engineering applications. Develop and analyze numerical algorithms, reduced-order models, and machine-learning-enhanced simulation
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/or R. Develop, maintain, and implement custom scripts for data acquisition, processing, and analysis as well as machine learning algorithms and techniques to classify and predict outcomes based
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to architectures, algorithms, and applications. Titled "Exploring Atomic Ensemble Qubits for Distributed Quantum Computing," this LBNL-funded initiative brings together experts from the ESnet, NERSC, and AMCR
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for communications metrics Hardware-aware evaluation (desired): participate in SDR/hardware-in-the-loop experiments and help bridge algorithm design with realistic RF/platform effects. Required qualifications Ph.D. in
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Intelligence (CHI-lab, https://www.x-labs.xyz) in the Division of Engineering, New York University Abu Dhabi, is seeking a highly motivated Post-Doctoral Associate to advance cutting-edge research in machine
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 25 days ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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, as well as the team’s work on applied mathematics and advanced numerics, via the ACT website (https://www.esa.int/gsp/ACT/) . You also are encouraged to visit the ESA website: https://www.esa.int
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modeling and simulation • Development in Finite element and alternative discretization methods (e.g. Lattice Boltzmann methods) • High-dimensional algorithms and high-performance computing
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of accomplishments (e.g., peer-reviewed publications and/or patents). Key Responsibilities & Accountabilities: Algorithm Development, Data Analysis and Communication: Effectively design, implement, and evaluate
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and complex system simulations develop, implement, test, and benchmark computational models, algorithms, data pipelines, and research software prototypes contribute to optimisation, simulation, sampling