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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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array of capabilities in nuclear nonproliferation, data analytics, cybersecurity, cyber-physical resiliency, geospatial science, and high-performance computing, our organization seeks to produce world
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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will focus on the design and implementation of photogrammetry and 3D computer vision algorithms to support high-volume and high-throughput scientific data applications, primarily with remote sensing
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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performance modeling, static analysis, or PIM/heterogeneous architecture research. Knowledge of large-scale scientific computing applications and algorithms (sparse linear system solvers, dense matrix
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system integration, as well as embedded controller development. Experience in modeling converter topologies and distribution grids, developing control algorithms, and implementing controller code is
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Requisition Id 16167 Overview: The Multiphysics Modeling and Flows (MMF) Group in the Computational Sciences and Engineering Division is seeking a Postdoctoral Research Associate with expertise in
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Ph.D degree in electrical engineering, computer engineering, computer science, or a related discipline Demonstrated experience developing, training, and applying AI algorithms to physical sensor data
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based