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Field
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fraction of the user base is studying batteries and battery materials. This X-ray Diffraction – Computed Tomography (XRD-CT) postdoctoral research position is hosted by the Division of Solid Mechanics
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, or zeolite synthesis and characterisation; (c) experience with standard catalyst characterisation techniques (e.g., XRD, gas physisorption, electron microscopy, or spectroscopic methods); (d) solid track
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the use of multi-technique characterisation of inorganic materials, including at least some of the following: pXRD, single-crystal XRD, SEM/EDX, DSC/TGA/STA, EXAFS, laser diffraction (PSD), ICP-OES, XRF
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Research Associate (Materials Science & Engineering / Mechanical Engineering / Chemical Engineering)
(EBSD), X-ray Diffraction (XRD), and optical microscopy etc. Mechanical Testing: Perform tensile, fatigue, hardness, and impact testing to evaluate the performance of the printed components. Data Analysis
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ability, Hands-on abilities in operating advanced characterization and testing instruments (TEM, Cs-correctedTEM, cryo-EM, XRD, universal tester, etc.). Experience: More than 3 years of management
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, phase transformations, TEM/STEM, in-situ TEM, STEM-EELS, EBSD, in-situ XRD, fatigue, fracture toughness, and nanoindentation. Additional Information: Location: Edison Joining Technology Center (0057
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to include: Experience in the use of multi-technique characterisation of inorganic materials, including at least some of the following: pXRD, single-crystal XRD, SEM/EDX, DSC/TGA/STA, EXAFS, laser diffraction
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Diffraction (XRD), Small-Angle X-ray Scattering (SAXS), Wide-Angle X-ray Scattering (WAXS), and X-ray Fluorescence Spectroscopy (XRF). Collaborate with USC research groups to apply advanced X-ray
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disciplines. Strong interest in physical metallurgy, thermodynamics, kinetics and phase transformations. Previous experience with phase transformations, SEM/EBSD, XRD, thermal analysis, modelling or scientific
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learning, materials characterisation or computational materials science. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis