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) applications. You will specifically apply these methods to dynamic reservoir modeling, area of review evaluation, and wellbore integrity assessment of GCS in both new fields and reuse scenarios. You will also
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conferences. Engage in community knowledge-sharing (e.g. tutorials for the NERSC user base). What is Required: PhD awarded within the last five years in Physics, Computational Chemistry, Computational Science
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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software Shape future NERSC supercomputers, evaluating new architectures for AI. Collaborate with scientists and industry partners to enable transformative AI for science Determine methods and procedures
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partners to develop and utilize novel characterization and diagnostic methods to identify critical performance and durability barriers for these electrochemical devices and develop hypotheses and strategies
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pass applicable training, including Respirator Fit Testing, formal HAZWOPER training, and other related emergency response training. Some duties of this position require up to 1 hour per day of the
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of noise and error sources in superconducting systems. Familiarity with benchmarking and characterization methods for quantum computers. Experience with tensor network methods and other scalable
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presentation skills Mentoring students Contribute to the organization of workshops and conferences What is Required: PhD degree in physics or a relevant field. A minimum of 2 years of documented postdoctoral
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development. Engage in seminars, workshops, and group meetings to broaden scientific perspective and support growth. What is Required: PhD in a technical field such as chemistry, physics, materials science
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of neural population dynamics. This position has the specific focus of developing ML methods to assess the feedback controllability of neural population dynamics recorded from brain organoids. This position