51 moeling-and-simulation-post-doc Postdoctoral positions at Oak Ridge National Laboratory
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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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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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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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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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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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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
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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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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
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