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emitters in silicon and 2D transition metal dichalcogenides. You will work in a highly collaborative environment that includes Berkeley Lab, UC Berkeley, Rice University, and Dartmouth, contributing
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the fundamental mechanisms of mineral interfacial chemistry, element and isotope partitioning, crystal growth, dissolution, and phase transformation. The project team uses state-of-the-art computational and
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characterization, fundamental interface and component fabrication (including catalyst, catalyst/electrolyte interfaces, and membranes), integration and simulation. The incumbent will collaborate with research
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for data analysis and predictive modeling. The fellow will join a small, world-class, multi-institutional team advancing microelectronics research through AI-enhanced methodologies. You will: Perform soft X
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+ experiment), ensuring reproducibility and shared analysis notebooks. Co-mentor Foundry users/interns during beam-time-style measurement campaigns. What is Required: Ph.D. in Physics, Applied Physics
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for near-real-time data analysis. Your work will help 12,000+ users run faster, more reliable science. What You Will Do: Contribute to one or more NESAP scientific workflows targeting NERSC HPC resources
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scientists to integrate state-of-the-art AI with simulation and data analysis, including modern agentic approaches. Publish and present results in peer-reviewed venues. Examples of NESAP project themes
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science, physics, mechanical engineering, applied math, theoretical neuroscience, or statistics. In depth experience with control theory and machine learning for analysis of neural population data. Experience with