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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
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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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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Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected
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-efficient building systems, thermal-fluid processes, industrial heating applications, data center thermal management, and other emerging energy technologies. Develop and operate experimental facilities
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manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed