73 data-mining-post-doc Postdoctoral positions at Oak Ridge National Laboratory in Postdoctoral
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manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
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data science to develop new methodologies for assessing and improving the quality of components fabricated using advanced manufacturing processes. This position resides in the Manufacturing Systems
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magnets, batteries, and semiconductors from mined resources and electronic waste, as well as separations for bioenergy applications. Develop new research directions and contribute to proposals for external
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illumination effects. Coordinate optical measurements with the imaging teams; provide calibrated data, metadata, and analysis to ORNL and university collaborators. Develop instrument-control and data-analysis
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methodologies as well as subsized mechanical testing methodologies on highly irradiated materials (either using ions or research reactor irradiation data) for establishing performance envelopes for materials
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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improve access to divertor-relevant target exposure conditions. Validate simulation results against experimental data and constrain code inputs to reduce uncertainty in predictions for future experiments
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. 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 the SPRUCE (Spruce and
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. 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 the SPRUCE (Spruce and
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. Excellent analytical skills with high attention to detail. Experience with geospatial data visualization tools such as ArcGIS, Cesium, or similar. Familiarity with urban-scale building energy modeling