21 postdoc-in-thermal-network-of-the-physical-building Postdoctoral positions at University of California
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Skip to main content Recruit Home Open Recruitments Postdoc-UCSB Physics Dept. (Experimental High Energy Physics on CMS Experiment)/Richman Research Group (JPF02921) Postdoc-UCSB Physics Dept
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Skip to main content Recruit Home Open Recruitments Postdoc - UCSB Physics Department (Exoplanets)/Bowler Research Group (JPF02906) Postdoc - UCSB Physics Department (Exoplanets)/Bowler Research
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Skip to main content Recruit Home Open Recruitments Postdoc-UCSB Physics Dept. (Galaxy Evolution)/Casey Research Group (JPF02904) Postdoc-UCSB Physics Dept. (Galaxy Evolution)/Casey Research Group
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University of California, Davis, Department of Physics & Astronomy Position ID: UCD -Physics&Astronomy -CMSPOSTDOCS [#28283] Position Title: Position Type: Postdoctoral Position Location: Geneva
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, lead large-scale benchmarking across the full stack, and develop scalable classical simulations (e.g., tensor networks)—including performance bounds beyond brute-force classical simulability. This role
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components from MEA devices. Apply fundamental principles to approach and address complex behaviors of MEA devices. Perform multi-physics simulation and modeling for water and CO2 electrolyzers. Work
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-reviewed venues and 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
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records of all research performed. Adhere with EH&S and ETA safety guidelines. Additional Responsibilities as needed: Pursue funding for additional research of mutual interest to the postdoc and an LBL PI
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resources, and the DOE ESNET network. Develop and apply advanced workflow capabilities that improve performance, portability, and productivity. Perform performance analysis and optimization across end-to-end
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research focuses on a geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics