99 data "https:" "https:" "https:" "https:" "UCL" "Brunel University London" positions 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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Term) Time Type Full time The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected
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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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-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and
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-system data, simulation platforms, and digital-twin or real-time environments as appropriate. Collaborate with researchers across power systems, controls, cybersecurity, and AI/ML to develop publications
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. Support APS operation by participating in experiments and processing and analyzing experimental and operational data using AI/ML methods. Foster strong, positive working relationships with APS technical
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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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/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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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