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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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Laboratory. This is an opportunity to be part of a team helping with the design of new system software and runtime infrastructure to improve the performance and energy efficiency of future scientific computing
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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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of MEMS devices. Understanding the properties of materials used in MEMS and their compatibility with X-ray optics applications. Knowledge of using software tools to simulate the physical properties and
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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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will facilitate the comprehensive characterization of microelectronics under various conditions, including thermal, mechanical, and radiation stresses. The software developed through this project will
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, specifically for low-carbon fuels (e.g., H2, NH3), when using commercial or open-source CFD software (e.g., CONVERGE or similar). Work in a multidisciplinary team involving engine modelers and computational
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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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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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equipped with world-leading full-field imaging instruments, including ultrahigh-speed imaging. The group also develops end-to-end scientific software, data analysis, and interpretation methods