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order and dynamics Analyzing data sets and interpreting results Preparing manuscripts for publication, and presenting results at scientific conferences Experimental planning, and preparing new beamtime
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will collaborate closely with NSLS-II staff while developing cutting edge sample preparation and data analysis techniques that enable the next generation of the XCFS methodology. In addition
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experiment at the EIC. The program includes data analysis involving polarized targets at Jefferson Lab as well as full detector and physics simulations for ePIC. In addition, the candidate will collaborate
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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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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 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