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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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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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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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; 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 last 0-5 years) in the field of electrical engineering, experimental physics, materials science, and mechanical engineering, or related discipline Experience in electron beam lithography and/or micro
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and contributing to reusable research software when appropriate. Position Requirements Required skills, experience and qualifications: PhD in computer science, applied mathematics, electrical
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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
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complexes Small gas-phase molecules and organic chiral compounds Compute accurate transition moments beyond the electric-dipole approximation and simulate x-ray observables, including x-ray absorption, x-ray
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) in the field of accelerator physics or a closely related science and engineering discipline Strong experience developing and applying computational modeling and simulation Familiarity with accelerator
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-completed PhD (within the last 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field. Comprehensive understanding of applied computational materials