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applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a
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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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will be part of a team that already uses machine learning to improve online accelerator models and that develops correction algorithms for accelerator operations. This position is for a 2-year research
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spectroscopic methods. Experience with basic synthetic methods, electrochemistry, and sample preparation under inert conditions. Kinetic modeling, thermodynamics, electron transfer. Environmental, Health & Safety
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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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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) Large Language Model (LLM) and Reasoning Language Model (RLM) for science and
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, and Abilities: Experience with neutron or x-ray scattering from single crystals Experience with characterizing magnetic and structural dynamics using neutron scattering Modeling neutron scattering from
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transport modeling and machine protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses
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Research program. The project aims to integrate a diverse suite of high-resolution observations (atmospheric, land surface, and infrastructure), diagnostic/predictive models, and civic engagement to provide
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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