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Field
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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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the adaptation and improvement of the algorithms required for the aggregation and provision of flexibility to the grid, the optimized management of an energy community, and the intelligent operation and demand
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vision, graphics and visualization, systems, distributed algorithms, and scientific and biomedical computing. Research in the department is and has been funded by numerous agencies, including the National
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processing algorithms from concept to implementation. Eligibility to obtain a United States SECRET clearance (or higher) is required for ongoing employment in this position Minimum Qualifications: 1
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control systems, especially EPICS. Familiarity with synchrotron light science (e.g., ALS) preferred. Experienced in RF technology, magnetics, distributed controls, and algorithm design. Strong academic
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with compiler level languages, accelerated computing (preferably with OpenMP or OpenACC) and distributed computing (MPI). A thorough understanding of the particle-in-cell algorithm, its inherent
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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group , led by Prof. Glenn van de Ven, is part of the Department of Astrophysics . The research group constructs detailed dynamical models of galaxies and stellar clusters to infer the distribution
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the power of cutting-edge digital technologies to implement and manage FAIR (Findability, Accessibility, Interoperability and Reusability) environments for data management and algorithm preservation and
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related