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of the student will be the performance of power grid modeling and simulation, statistical analysis and machine learning applications in power system control or cybersecurity, and the implementation in Python and
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deployment strategies to evaluate and inform next-generation predictive urban climate models (e.g., using OSSEs (Observing System Simulation Experiments) or ablation studies, through extensive literature
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Develop a prototype neural network model for modeling strongly correlated materials. Implement and experiment with models using PyTorch and TensorFlow frameworks. Collaborate with team members to evaluate
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Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes
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, Beyond the Standard Model phenomenology, precision calculations for the LHC and intensity frontier, overlaps of particle physics and cosmology, and lattice gauge theory. The lattice efforts span weak
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and dynamic structures, lifting and positioning fixtures, high voltage and high current magnetic and electrostatic devices, Radio Frequency (RF) accelerator cavities and components, vacuum components
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, and infrared light to enable discoveries in clean and affordable energy, high-temperature superconductivity, molecular electronics, and more. Position Description The Data Science and Systems
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optimization. Basic knowledge of integrated circuit design, including digital simulation and logic synthesis. methods, and other related topics pertaining to fast AI model inference. Experience working in
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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) Large scale foundation model for science and engineering; (ii) Causal
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on the challenges presented by analyzing, interpreting, and using data at extreme scales and in real-time. The data science program is accompanied by significant computational modeling research efforts supporting