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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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architectural models, system-level simulators, and performance modeling frameworks for QHPC systems, capturing relevant characteristics of quantum processing units, classical HPC resources, interconnects, system
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team planning, documentation, verification and validation, and manufacturing-material-related simulation quality processes Author peer reviewed papers, technical papers, reports and proposals
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material, device, and process scales Plan and execute experiments to support and augment modeling efforts Conduct simulations using state-of-the-art tools to supplement model development Contribute
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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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, SEM, or Raman. Perform data analysis of mass spectrometry (i.e., GC-MS, LC-MS, SIMS) and microscopy data sets and coordinate between experimental studies and theoretical simulations. Communicate
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characterization of polymers to maximize material performance but also close collaboration with multidisciplinary teams to scale up manufacturing processes for high-performance fibers. A key aspect involves
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system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our