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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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, Astrophysics, Physics, Cosmology, or a related quantitative field (e.g., Applied Mathematics, Computer Science, Statistics, Data Science) Demonstrated research experience in observational cosmology or wide-field
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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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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, reproducibility, and scalable data understanding Position Requirements PhD completed within the last 0–5 years (or near completion) in Computer Science, Computational Science, Visualization, Human–Computer
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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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external partners Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, chemistry, or physical chemistry Demonstrated expertise in theoretical quantum
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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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