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Position Details Position Information Internal Posting? Posting Number SP005014P Position Title Postdoctoral Research Associate, Statistical and Computational Modeling for Digital Twins Division
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imaging, digital pathology, and spatial omics datasets for research projects. - Support computational image analysis, data annotation, and quality assurance activities using established software tools and
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lab techniques: classical diagnostic parasitology techniques, microscopy, parasite culture, in vitro assays, DNA and RNA extraction, PCR, RTPCR, qPCR, qRT-PCR, digital PCR, and ELISA. Experience with
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 6 days ago
that typical experimental characterization methods (e.g., electron backscatter diffraction, digital image correlation) provide only surface-level grain structure information, while the subsurface
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Lehigh University Plasma Control Group (LU-PCG) and contribute to advanced control synthesis, neural observer development, scenario optimization, and AI-enabled digital twins for magnetically confined
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-particle physics, predictive modeling, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. The research will combine high-fidelity simulations, reduced transport modeling
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 6 days ago
The project will leverage NASA’s imaging spectroscopy archives, complementary multi-mission satellite and airborne platforms, and high-resolution digital topography. Candidates should have interest in using
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 24 hours ago
microstructure evolution. The approaches include but are not limited to continuum, dislocation dynamics, phase field, crystal plasticity finite element method, physics-informed digital twins, and/or data-driven
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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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technologies, digital soil mapping/pedometrics, proximal/remote sensing, and geospatial modeling. The ideal candidate will work and collaborate closely within a research team at Texas A&M AgriLife and USDA-ARS