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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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The position is part of a new collaboration between Argonne National Laboratory, the University of Notre Dame, and UIUC, supported by the Quantum Information Science Enabled Discovery 2.0 (QuantISED
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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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-completed PhD (within the last 0–5 years) with strong background in physical chemistry, photophysics, photochemistry, and polariton chemistry Hands-on experience in thin-film deposition, and spectroscopic
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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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, supply chain risk analysis, and data-driven modeling (including AI/ML where appropriate) to help inform decision-making for energy deployment and national competitiveness. In this role you will : Conduct
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contribute to open-source code repositories and documentation. Position Requirements Required skills, knowledge and qualifications: PhD in physical oceanography, coastal engineering, computational science
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, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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superconducting RF (SRF) technology. Since then, a transformative SRF approach using Nb₃Sn has emerged, offering performance comparable to niobium while enabling operation at higher temperatures—potentially