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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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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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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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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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algorithms for behavioral cue extraction and novel approaches for the modeling people interaction, with application to medical research and affective computing. Responsibilities: Write code and develop novel
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. Of particular interest is the modeling of transport networks across multiple scales, including their function, development and remodeling. We employ advanced computational and theoretical techniques, such as
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Laboratory (ORNL). Major Duties/Responsibilities: Develop models and software for the design and optimization of sensor networks in complex systems, such as water treatment systems Develop physics-based and
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provide insights into comparative physiology across different species. Of particular interest is the modeling of transport networks across multiple scales, including their function, development and
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. We have multiple NIH-funded projects to study the influence of FDA-approved GLP-1R agonists on drug seeking and reinstatement in rodents. A newly funded NIH R01 has the goal to determine who may be