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Argonne National Laboratory invites applications for a Postdoctoral Appointee to advance the deployment, commissioning, and scientific use of Transition-Edge Sensor (TES) X-ray microcalorimeter
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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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of reaction mechanisms in molten salts and apply insights to process development and scale up. Project activities will include the design and development of advanced sensors and flow systems for molten salts
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(e.g., electrochemical and optical sensors) for molten salts, the development of innovative processes and technologies for recycling actinides to support sustainable fuel cycles, and the application
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to assess evolving risks in coastal-urban regions. Other key responsibilities include: Mesh design and high-resolution data utilization. Develop and refine high-resolution barotropic ocean meshes along U.S
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detectors while also having flexibility to pursue your own research interests. Research Focus Participate in a detector R&D program aimed at developing superconducting nanowire sensors to enable
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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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(BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology. Develop in-situ and
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Biology: Strong background in systems biology and regulatory network modeling Interdisciplinary Collaboration: Experience working across disciplines with computational biologists, computer scientists, and