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on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation
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simulation, AI based detector design optimization, streaming computing model development, production, distributed computing and workflow management, software infrastructure, particle ID, tracking
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) applications in the power grid. • Experience with power system modeling, simulation, dynamics and/or optimization, phasor and electromagnetic transient (EMT)-based modeling, and Python and/or Matlab
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scientific and security problems of interest to Brookhaven Lab and the Department of Energy (DOE). Topics of particular interest include novel development and application of machine learning models, especially
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senior ASIC designer for developing Monolithic Active Pixel Sensors (MAPS) - based Silicon Vertex and Tracking (SVT) detector technology for the collider experiments such as lepton-lepton Future Circular
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foundational ML and NLP innovations. This work involves the development and applications of encoding and generative NLP models (e.g., large language models, LLMs), and includes model training, tuning and
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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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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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and imaging x-ray tools, supported by computational resources for high-throughput analysis and modeling. The program’s scientific focus is to understand and optimize the structural and chemical features
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, processing or environmental conditions relevant to diverse areas such as energy, health, microelectronics, geology, environment and planetary sciences. Using various electron and photon-based spectroscopic