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The Nanoscience and Technology Division (NST) at Argonne National Laboratory invites applications for a postdoctoral researcher to lead cutting-edge efforts in electrically driven ultrafast electron
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fabrication facilities Access to the Center for Nanoscale Materials (CNM) Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in physics, electrical engineering, materials
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current sensors) Develop and characterize superconducting nanowire single-photon detectors (SNSPDs) using high kinetic inductance materials such as NbN, TiN, and NbTiN, targeting high detection efficiency
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with strong expertise in machine learning for cyber-physical systems and a solid understanding of electric power distribution systems, and microgrid operations. The selected candidate will develop and
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avenues will be pursued: advancing our toroidal anvil capabilities to extend the maximum achievable pressure ranges; and depositing designed electrical circuits to create advanced multi-modal experiments
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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it in Physics with a focus on accelerator physics, or Electrical Engineering with a focus on RF/Accelerator Physics Strong background in accelerator physics and beam diagnostics. Excellent problem
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-world problems. Position Requirements Recent or soon-to-be completed (typically within the last 0-5 years) PhD in Electrical Engineering, Industrial Engineering, Applied Mathematics, or a closely related
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to current MSR developers for use in developing molten salt fuel specifications. Develop novel and improved methods for measurements of molten salt properties and standardize procedures to ensure quality data
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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model