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of multi-omic data Programming Proficiency: Strong knowledge of Python, C/C++, Julia, and other relevant programming languages Ability to model Argonne's core values of impact, safety, respect, integrity
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related discipline. Expertise in Nek5000/NekRS or other comparable spectral element method codes. Experience in running high-fidelity simulations on leadership class supercomputers. Knowledge of performing
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completed in a relevant field of study. Knowledge of x-ray/optical/electron physics, including diffraction, optics, detectors, scattering etc. Experience with deep learning (DL) libraries such as Tensorflow
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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communication skills, and ability to interact with people at all levels both within and outside the laboratory. Preferred Knowledge, Skills, and Experience: Experience with scientific AI techniques like Physics
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, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience with THz or mmWave instrumentation and measurements. Familiarity with high-gradient acceleration and wakefield accelerator
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the ultrathin limit. Position Requirements We seek outstanding researchers with a strong background in experimental condensed mater physics and materials science. No prior knowledge of MBE is needed, though a
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to interact with people at all levels both within and outside the laboratory. Preferred Knowledge, Skills, and Experience Experience with X-ray scattering/diffraction and quantitative data analysis and modeling
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. Preferred Knowledge, Skills, and Experience Prior experience with high-throughput or computational protein design/screening techniques. Background in structural biology (CryoEM/crystallography) Knowledge
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values of impact, safety, respect, integrity, and teamwork. This position requires an on-site presence at the Argonne campus in Lemont, Illinois, five days per week. Preferred Qualifications: Knowledge in