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Field
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creating the fastest algorithms for modeling of entanglement purification circuits; creating the highest performance quantum error detection circuits; developing symbolic computer algebra systems for quantum
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Professor Lei Xie seek to hire Postdoctoral Research Associates to train in and oversee in interdisciplinary projects involving AI algorithm development, physiologically based pharmacokinetics, computational
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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algorithms (convex/nonconvex, stochastic/robust, MPC) for real-time dispatch, frequency regulation, and DER coordination. Integrate data-driven and physics-informed approaches for state estimation, forecasting
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 10 hours ago
have experience with algorithms, numerical techniques, and computational methods, specifically for uncertainty quantification, Bayesian statistics, and multivariate optimization; must have excellent
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and