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physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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configurations. The ability to characterize products using GC-MS, FTIR, Raman, NMR, TGA-DSC, and LC-MS. Experience with X-ray absorption and scattering techniques. Experience in catalyst design and synthesis. Job
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, design of experiments, image and data processing. Position Requirements Recent or soon-to-be-completed PhD with strong background in Physics or Materials Science (within the last 5 years) Physics
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Comprehensive knowledge of imaging systems and/or semiconductor or solid-state detector design and simulation Experience with GEANT4 simulations and detector characterization is highly desirable Familiarity with
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., specific code you wrote, modules you debugged, or workflows you designed). Highlight Transferable Skills: If your background is in a specific science domain (e.g., Physics, Biology), frame your experience in
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, ptychography, Laue microdiffraction, or related coherent/imaging techniques. Proven ability to design, conduct, and analyze complex synchrotron experiments. Proficiency in scientific programming (Python, MATLAB
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accelerator structures, as well as conducting beam-based experiments. Key Responsibilities: Assist in the design and testing of beam diagnostic device operating at mmWave frequencies. Assist in conducting beam
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, advanced characterization, nanofabrication, and theory/modeling. Key Responsibilities: Perform transmission electron microscopy and spectroscopy, including in situ and ultrafast measurements Design and
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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML