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Apply Now Job ID JR101405Date posted 09/12/2024 The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates
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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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. The program involves close collaborations with experts in theory and data science and will benefit from frequent interactions with principal investigators at the National Synchrotron Light Source II (NSLS-II
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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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for highly motivated postdoctoral researchers to conduct advanced electron microscopy studies on quantum materials for neuromorphic computing. The primary objective of this research is to explore quantum
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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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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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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
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for the characterization. Prepare research based manuscripts for publication Participate in the meetings related to the EFRC Required Knowledge, Skills, and Abilities: PhD in Condensed Matter Physics, Materials Science
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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