45 combustion-modelling-postdoc Postdoctoral research jobs at Oak Ridge National Laboratory
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Requisition Id 16167 Overview: The Multiphysics Modeling and Flows (MMF) Group in the Computational Sciences and Engineering Division is seeking a Postdoctoral Research Associate with expertise in
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performance models. This position resides in the Materials Engineering Group in the Large-Scale Structures Section, Neutron Scattering Division, Neutron Sciences Directorate at Oak Ridge National Laboratory
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), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop physics-based computational models, including Finite Element Analysis (FEA
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workloads. Conduct research on language front‑end abstractions, mixed‑precision modeling, heterogeneous parallelism, and MLIR-level transformations. HPC System Co‑Design: Work with domain scientists and
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-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training
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the last 5 years Preferred Qualifications: Strong background in experimental systems related to heat and mass transfer systems. Knowledge of CFD tools and analytical modelling is preferred. Experience with
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and