90 data-"https:" "https:" "https:" "https:" "University of Cambridge" Postdoctoral research jobs at Argonne
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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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facilities. Perform thermochemical and thermophysical properties measurements of MSR relevant molten salts to understand the impact of evolving salt composition on property values and provide this data
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microwave resonator design, characterization, and RF/microwave measurement techniques Electromagnetic simulation experience (e.g., Sonnet, Ansys HFSS/Lumerical, or similar tools) Experience with data
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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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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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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, design of experiments, image and data processing. Position Requirements Recent or soon-to-be-completed PhD with strong background in Physics or Materials Science (within the last 5 years) Physics
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/modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a closely related field
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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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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