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a related field Experience with radiation transport codes (e.g., FLUKA, Geant4, MCNP etc.) Excellent programming and data analysis skills (e.g., Python, C++, or similar) Solid understanding
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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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studies and computer simulations Collaborate with the BMAD development team at Cornell University by implementing new features into the code Participate in the EIC design effort in a more general sense
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses
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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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.) and electrical device data analysis including transistor characteristics. You communicate effectively, verbally and in writing, evidenced by peer-reviewed publications and conference presentations
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and relevant data analysis. • Demonstrated experience in Python programming. • Knowledge of machine-learning algorithms. Additional Information: BNL policy requires that after obtaining a PhD
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to unique data sources, will ensure that the successful candidate has the necessary resources to solve challenging DOE problems of interest. The successful candidate will join a growing research group with
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facilities, as well as collaboration opportunities with domain scientists and security experts. Access to these platforms will allow computing at scale, and together with access to unique data sources, will
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data sources, will ensure that the successful candidate has the necessary resources to solve challenging DOE problems of interest. The successful candidate will join a growing research group with diverse