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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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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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. 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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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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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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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
5 years or soon-to-be-completed in physics, materials science, chemistry, chemical engineering, or a related field. Demonstrated expertise in synchrotron-based XFM or related X-ray microscopy methods
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs