60 parallel-computing-numerical-methods "DIFFER" Postdoctoral positions at Oak Ridge National Laboratory
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teams that are composed of ORNL researchers with various backgrounds (e.g., material science, neutron science, chemistry, manufacturing, computing, others). Interact and collaborate with researchers
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models, and representative scenarios using multimodal transportation, vehicle, infrastructure, fleet, and energy data. Develop AI-based control and optimization methods for connected vehicles and smart
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ecosystem processes. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from
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Duties/Responsibilities: Perform hands-on synthesis and characterization of novel organic compounds to meet project goals Use a variety of methods to characterize and evaluate newly synthesized
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trait databases to assess ecosystem response to environmental forcing. The primary research focus is AI-forward, experiment-driven. This position encourages leveraging AI/ML methods to assess plant
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modeling, and process optimization. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of
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lasers, optics, and optical emission spectroscopy. Experience designing custom experimental hardware, vacuum systems, and plasma diagnostics. Experience applying machine learning methods for
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security objectives. MCG represents expertise in nuclear materials synthesis and analysis, deployment of advanced analytical methods including crystallography, imaging, spectroscopy, physical property
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and recovery methods for Gallium and other critical materials. Major Duties/Responsibilities: Design and synthesize inorganic materials, especially ion intercalation compounds. Design and execute
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-equilibrium physics. Experience in phenomenological and/or computational methods for quantum/classical dynamics in complex systems. Preferred Qualifications: Rich experience with modeling in spin or