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Field
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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sampling-based and reinforcement learning-based motion planning algorithms for multiple robotic arms in automotive manufacturing, including testing, performance evaluation in both simulation and actual
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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not
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) for the high-luminosity phase of the LHC, in particular on its mechanical design, on the generation of the L1 trigger primitives, and on the development of offline reconstruction algorithms. In addition, it is
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work with the world's first exascale system
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users, researchers, and developers, both internally at the University of Sydney and externally across multiple institutions publish research papers on mathematical theory and the development and analysis
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resiliency, and energy management algorithm development using MATLAB/Simulink for marine microgrid applications. Knowledge on control is highly preferred. Have experience and commitment to supervising student