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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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transition metal complexes, donor-acceptor pairs, and hybrid molecular-material interfaces. By designing and deploying state-of-the-art ultrafast nonlinear optical spectroscopy techniques—such as transient
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. This position offers an exciting opportunity to work at the intersection of HPC and AI, addressing critical communication bottlenecks and optimizing network interconnects for large-scale distributed systems
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communication skills. Collaboration skills and the ability to work with a team in an interdisciplinary environment. Good: Direct research experience in near field optical dynamics, nonlinear optics, and optical
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(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based
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algorithms to develop cybersecurity, optimization, and control solutions for real-world grid applications. Candidates will be required to work in at least 4 of the following areas: Build, simulate, and
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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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on integrating and analyzing diverse data types to support scientific discovery Research and implement data management solutions using advanced storage systems Evaluate and optimize AI applications for performance
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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-micron resolution of entire organs demands optimized data pipelines and methods to handle and visualize the resulting datasets. Additionally, beamline hardware must be optimized to ensure the highest data