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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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rules. Ability to work with large volumes of hazardous chemicals. Flexibility to change projects and work on a variety of projects simultaneously Ability to model Argonne’s core values of impact, safety
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to work in a team environment. Skills in installing, modifying and maintaining equipment. Willingness to abide by safety rules. Ability to work with large volumes of hazardous chemicals. Ability to change
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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), and cell free methods. Key Responsibilities: Development and optimization of vector constructs and expression condition characterization of protein yields and quality, and large-scale protein production
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with
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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