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Field
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include deep learning, reinforcement learning, differentiable modelling and inverse design. You will implement and evaluate these methods using experimental optical systems and work towards their
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discrete optimization, computational optimization, inverse problems, and optimization methods for scientific and engineering applications. Dynamical and complex systems: nonlinear dynamics, mathematical
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, high-performance scientific computing, tensor methods, and scalable algorithms. Optimization: continuous and discrete optimization, computational optimization, inverse problems, and optimization methods
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
research opportunity invites a participant to investigate that coupling in both directions in support of NASA's microwave remote sensing missions. In the forward direction, physically based inversion of soil
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strategies that are robust with respect to perturbations and capable of balancing multiple competing performance criteria. Information Uncertainties are inherent in many engineering design problems and can be
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theories in inverse/scattering problems. Once successful, the candidate is expected to transfer his expertise to machine learning/scientific computing in collaborations with other group members (phd students
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Laplace transforms: the Laplace transform of elementary functions, properties of the Laplace transform, inversion of the Laplace transform, solving differential equations using Laplace transforms and the
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modeling and inversion studies Publish high-impact research articles in leading international journals Assist in the preparation and development of competitive research proposals Support the supervision and
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NIST Nanostructure Fabrication and Measurement Group | Gaithersburg, Maryland | United States | about 2 hours ago
, or other microscopy methods; instrument-sample interaction modeling; nanofabrication; inverse problems; statistics and uncertainty quantification; scientific machine learning; semiconductor metrology
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling