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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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position to work on development and scaling of the data infrastructure and software for AI applications on supercomputing systems and AI testbed systems. The postdoc will work on multimodal data management
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experts across system software, power management infrastructure, performance characterization, networking, and novel computer architectures and accelerators. It will also involve collaboration with leading
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Software-Defined Networking (SDN) solutions to dynamically manage network congestion and improve communication efficiency. Research and develop topology-aware collective communication algorithms to optimize
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methods and software for the analysis and calibration using very large data sets resulting from dynamical simulations on high-performance computing resources. Application areas include : 1. Epidemiology, 2
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evaluate distribution networks in OpenDSS; investigate feeder- and system-level impacts of DERs (e.g., load flow, hosting capacity, voltage regulation). Develop and refine T&D co-simulation platforms (e.g
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that are broadly applicable across the physical sciences but applied initially to x-ray characterization needs. They will publish results in high impact journals, present at conferences and work with the software
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, networking, and leadership. Position Requirements Required Knowledge, Skills, and Experience: This level of knowledge is typically achieved through a formal education in economics, operations research, public
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or more of the following: Experience with large-scale molecular dynamics (MD) simulations using software such as LAMMPS. Experience in handling and data analysis generated from multi-million to multi
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heterogeneous Catalysis focusing on stimulus driven and non-equilibrium catalysis Design, synthesize, and characterize well-defined complexes supported on metal oxide or non-traditional support materials