65 combustion-modelling-postdoc Postdoctoral positions at Oak Ridge National Laboratory
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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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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, instrumentation, and data acquisition systems; conduct laboratory and field testing; and perform thermodynamic analysis, system modeling, and performance assessments. Analyze and interpret experimental and modeling
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credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
experimental thermal/fluid systems, (b) mechanical/chemical systems and instrumentation, (c) fluid dynamics, combustion, and heat transfer, and (d) thermal and mechanical characterization. Excellent written and
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Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
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theory, thermodynamics, statistical mechanics, or non-equilibrium physics Preferred Qualifications: Rich experience with transport measurements and characterization Basic knowledge of models of strongly
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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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for radiation protection and develops many of the biokinetic and dosimetric models recommended by the International Commission on Radiological Protection (ICRP) and applied by U.S. federal agencies The successful
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful