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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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growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans. The successful candidate will apply methods from economics, supply chain risk analysis, and data
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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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-completed PhD (within the last 0-5 years) in field of physics, engineering, or a closely related field Demonstrated programming proficiency in C/C++, Python, or another scientific programming language
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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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., transformer-based models, tokenization, embeddings) to HEP analysis Communicate results internally and externally through talks, notes, and publications Position Requirements Recent or soon-to-be-completed PhD
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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