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networks, online analysis with delay, and theory of distributed algorithms. Job description In our group, we try to apply and unite the approaches and techniques of theory and practice. Some members of our
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that develops advanced AI compute solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid hardware architectures
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the values are drawn independently from known distributions. These results have been widely applied in areas such as auction theory, resource allocation, and stochastic optimization. The interdependent value
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, computer 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
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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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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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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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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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