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methodologies. Experience with data analysis and computer programming in C/C++/Python, and ideally familiarity with ROOT and Geant4 A demonstrated ability to perform research individually and as part of a
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spectroscopy and particle detection techniques, including relevant instrumentation and data acquisition methodologies. Experience with data analysis and computer programming in C/C++/Python, and ideally
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group analyzing deep soil warming experiments across the world. In this exciting role, you will apply mechanistic and machine learning models to study observed effects of warming on soil and ecosystem
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and machine learning models to study observed effects of warming on soil and ecosystem carbon cycling and the mechanisms shaping the soil response, and to estimate potential soil carbon feedbacks with
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electron tomography data. Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in
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evaporation, use of simple photolithography, furnace annealing, and rapid thermal annealing (RTA). Test cell fabrication using computer-controlled machine tools. Quantitative analysis of PEC data, application
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(ICP-MS). Perform test cell fabrication using computer-controlled machine tools. Data workup and analysis using standard computational tools such as Matlab and/or python programming environments. Work
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diverse team. Desired Qualifications: Experience with Li-ion battery and/or fuel cell and/or electrolyzer research. Computer programming skills. Electrochemical systems modeling. Experience in electrode
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scientists that will design and deliver novel algorithms, error mitigation and compiling techniques for DOE relevant science domains that will break new ground in modeling dynamical processes physical sciences
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The Computer Architecture Group and the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab have an exciting opportunity for a Postdoctoral Research Scholar to develop novel