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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, structural engineering, or a closely
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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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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regular channels. Position Requirements A formal education in chemical engineering, mechanical engineering, or a related field at the PhD level with zero to five years of experience. To perform
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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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monitoring and gradient tests. Participate in training opportunities, including attending the US Particle Accelerator School (USPAS). Position Requirements PhD completed in the past 5 years or soon to complete
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in high-impact venues Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of High Energy Physics or Nuclear Physics, or a closely related discipline
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-world problems. Position Requirements Recent or soon-to-be completed (typically within the last 0-5 years) PhD in Electrical Engineering, Industrial Engineering, Applied Mathematics, or a closely related
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-completed PhD with strong background in Materials Science or Physics (within the last 5 years) Considerable experience in understanding magnetic-domain physics in thin film and/or nanostructured materials