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. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
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facilities in Argonne National Laboratory, including the Advanced Photon Source and the Center for Nanoscale Materials, and integrates expertise in ultrafast optics, accelerator physics, and condensed-matter
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morphology, product quality, and process efficiency at engineering scales. With guidance, the appointee will : Perform experiments with multimodal sensors to advance the technical understanding and application
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The Center for Nanoscale Materials (CNM) and the Materials Science Division (MSD) at Argonne National Laboratory are jointly seeking a highly motivated postdoctoral researcher to advance quantum
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. The postdoctoral researcher will work closely with scientists at the Materials Science Division and the Advanced Photon Source (APS) as well as collaborate with external partners to exploit state-of-the-art
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Roman Space Telescope in the Cosmic Frontier. The Division is also engaged in physics preparations for the Electron-Ion Collider (EIC), the development of advanced accelerator technologies (such as the
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of spin qubits in semiconductor devices, with advanced x-ray characterization and spin measurements of such devices. These systems include SiC spin defects and Si/SiGe quantum dots. A successful candidate
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) of electrochemical energy storage devices (diagnosis) and predict the SOH into the future (prognosis). The primary projects this postdoc will contribute to relate to lithium-ion batteries, advanced lead-acid batteries
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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). About Argonne and the Physics Division: Argonne is a multidisciplinary science and engineering research center, where talented scientists and engineers work together to answer the biggest questions facing