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). This position focuses on The investigation of coupled land-river-ocean processes in coastal flooding applications by developing a coupled E3SM configuration that incorporates a subgrid-scale version of the MPAS
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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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insights and develop reduced order models (ROMs) for boundary layer flows and turbulent combustion. Integrate ROMs with CFD solvers and demonstrate predictive accuracy compared to traditional modeling
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against DFT. Atomistic modeling of reactions on metal oxides, including electrochemical reactions. Understanding of interfacial thermal transport, thermal boundary conductance, and phonon coupling across
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the ultrathin limit. Position Requirements We seek outstanding researchers with a strong background in experimental condensed mater physics and materials science. No prior knowledge of MBE is needed, though a
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The Theory Group in the Physics Division at Argonne National Laboratory is now seeking candidates for postdoctoral positions in nuclear theory, to begin as early as Spring 2026. The positions