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supporting end-users of applied ML methods including interpretability/explainability (XAI), and reliability. The position provides access to world-class computing resources, such as the BNL Institutional
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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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be overseen by Professor Yimei Zhu and emphasizes strong collaborations within the DOE-EFRC — “Quantum Materials for Energy Efficient for Neuromorphic Computing.” The position is ideal for candidates
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reduction and other chemical transformations, and the radiolysis of non-aqueous media that are used in energy applications, e.g., ionic liquids and alkyl carbonates. The position will make heavy use
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for scientific and security applications; (ii) ML model optimization for inference speed and deployment for real-time analysis; and (iii) AI model training for analog in-memory computing. The position provides
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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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these applications to inform new foundational ML and NLP innovations. The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership computing
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at the Computational Science Initiative (CSI), within the Brookhaven National Laboratory. The selected candidate will collaborate on solving inverse problem, relevant for interference lithography process, by deploying
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and uses these applications to inform new foundational ML and NLP innovations. The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE
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investigators. Position Requirements Ph. D. in theoretical or physical chemistry, or a related field Extensive experience in one or more of the following areas: Computational modeling of homogeneous