89 machine-learning-"https:"-"https:"-"https:" "Computer Vision Center" Postdoctoral positions at Oak Ridge National Laboratory
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in
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expertise 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
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for successfully generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning
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for successfully generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning
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complex challenges in science, energy, national security, and health. Our researchers develop scalable machine-learning models, deep-learning architectures, and data-driven methods for platforms ranging
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automated vehicles; AI or machine learning for transportation; traffic modeling, control, routing, or optimization; transportation digital twins and simulation; vehicle energy modeling; heavy-duty vehicle
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Carlo, machine learning or a related field Proficiency in quantum mechanics, statistical modeling, and linear algebra Preferred Qualifications: Experience with density matrix renormalization group and
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., machine-learning emulators, surrogate modeling, AI-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive