96 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Univ" positions at Argonne
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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
to the ISAAC data repository by generating AI-ready physical descriptors and advancing data-driven understanding of dynamic catalytic processes. Responsibilities include : Identifying relevant user systems and
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The Physical Sciences and Engineering Directorate seeks an outstanding Scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and
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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking an outstanding staff scientist to lead and support cutting-edge research at the intersection of AI/ML, data
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independently conducting machine studies to diagnose and resolve operational issues. Support APS performance improvements by conducting accelerator experiments, processing and analyzing data, and performing
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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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optical transition and favorable spin properties of individual solid-date erbium ions (Er3+) to store quantum information necessary for practical, robust, and scalable quantum communication. The focus
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user