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(CFD) to develop and optimize new processes and equipment designs using high-performance computing Develop process- and facility-scale models as the foundation for digital twins of chemical processing
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Position Requirements Ph.D. in Materials Science, Physics, Electrical Engineering, Applied Physics, or a related field (completed or soon-to-be-completed) Demonstrated expertise in nano- and mesoscale
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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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science. Position Requirements Ph.D. (completed or soon to be completed prior to the start of the appointment) in Physics, Materials Science and Engineering, Electrical Engineering, or a closely related
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics—ideally in accelerator science or engineering—or a closely related field Demonstrated experience or strong interest
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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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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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; publish and present high-impact research results Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of materials science, physics, electrical engineering, or a
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the past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline. Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing