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The PhD project will involve both (a) developing foundational probabilistic machine learning methodology, and (b) applied collaboration with experimentalists in chemistry and biology. The candidate may have
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, particularly GBS, continuous-variable QC Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical
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Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical experiments. Interest in connecting theory
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to provide dynamic transformer rating, and how to use batteries in distribution grids. Using e.g., the concepts of the Smart-Energy OS, which is a hierarchy of methods for aggregation, forecasting and control
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experimental and simulation data. Use machine learning techniques to identify trends, forecast behavior, and optimize performance metrics in PtX systems. Publish Research Findings: Disseminate research outcomes