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enrichment devices for processing uranium-bearing and stable isotope compounds. The Mechanical Systems Modeling Group applies first-principles physics and empirically informed methods to advance
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/device models into open-source software tools for integrated system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by
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) and Computational Fluid Dynamics (CFD), for polymer composite manufacturing processes Perform multi-physics simulations involving coupled thermal, mechanical, and material behavior across multiple
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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technology focused on gas testing of prototype enrichment devices for processing uranium-bearing and stable isotope compounds. The Mechanical Systems Modeling Group applies first-principles physics and
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Requisition Id 15997 Overview: We are seeking a postdoctoral researcher who will focus on atomistic simulation and data science approaches. This position resides in the Chemical Transformations
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planning using operation model development, either through development of bespoke simulation/optimization tools, or through application of tools like RiverWare, WEAP, WRAP, StateMod, OASIS, WRIMS, and HEC
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implement hybrid approaches that integrate process-based simulations with data-driven methods to advance hydrologic process understanding and prediction. Integrate diverse datasets (e.g., in situ observations
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Simulation & Data Processing: Use and extension of Allpix2 and TCAD-based simulation tools. Generation of large simulation datasets for algorithm training and validation. Integration of simulation with
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi-modal datasets. Develop AI/ML approaches to bridge length- and time-scales in