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associate position with a focus on natural language processing (NLP). This extremely fast-moving and competitive field has produced innovations with highly visible impact in industry, education, and public
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associate position with a focus on national security applications of natural language processing (NLP). This extremely fast-moving and competitive field has produced innovations with highly visible impact in
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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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work closely with CFN Electron Microscopy group members and computer scientists at Brookhaven. You will be professionally mentored by Dr. Judith Yang and Dr. Sooyeon Hwang and receive guidance from Prof
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of existing ones for scientific applications; (ii) Large Language Models (LLMs) and multi-modal Foundation Models (iii) Large vision-language models (VLM) and computer vision techniques; and (iv) techniques
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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) novel development of deep learning ML models and adaptation of existing ones
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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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novel genetic pathways regulating local and systemic acquired resistance, as well as growth-defense trade-offs. Using bacterial, fungal, and viral disease models, the research will focus on identifying