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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and
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, including parallel computing frameworks, C/C++, Julia and/or python Experience with training AI surrogate and/or inverse models Experience working in underground laboratories and/or clean rooms Excellent
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, sorption systems, compressors, heat exchangers, HVAC systems, and related technologies. Develop and apply system-level models to support research and design activities. Prepare technical reports, journal
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performance tools Study the performance, resiliency, power, and efficiency of modern and future high-performance computing systems under various workload characteristics through measurement, modeling, and
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific