58 moeling-and-simulation-post-doc Postdoctoral positions at Oak Ridge National Laboratory
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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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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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Requisition Id 15885 Overview: We are seeking a Postdoctoral Research Associate – Simulation and Machine Learning for Composite Manufacturing who will focus on developing physics-based simulation
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Requisition Id 15721 Overview: We are seeking a Postdoctoral Research Associate who will contribute to the development and implementation of novel quantum algorithms for materials simulation, with
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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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post-doctoral research associate to simulate amorphous materials and crystallization reactions using atomic-scale simulations. As a post-doc, you will utilize high performance computing and rare event
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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 numerical algorithms for modeling and simulation of nuclear systems. Computational Nuclear Engineers within the RTHPCM group will work with group, section, and division members and external
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Requisition Id 16016 Overview: Water and energy systems are deeply intertwined, and advancing science at their intersection is critical to the nation's future. We are seeking two Post-doctoral
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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