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Field
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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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. 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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to execute hard real-time control strategies for various power converters and grid interfaces. Develop test plans and conduct lab testing and real-time simulations (e.g., HIL) to verify control algorithms and
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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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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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on results. Document development processes, models, and algorithms for future reference and reproducibility. Ensure compliance with data privacy and security regulations in all development activities. Any
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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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develop dataset processing algorithms, for in-acquisition and proprietary datasets, including in federated training settings. Day-to-day: Develop new ideas, write code, run experiments, analyze data, and
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, interoperability, and compliance with emerging grid standards. Key Responsibilities: Design and develop control algorithms for grid-forming converters. Conduct simulation and experimental validation using real-time