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teams to codesign hardware, algorithms, benchmarks and software for QHPC systems, aiming to advance our strategic goals in leveraging quantum computing and high-performance computing (HPC) to develop
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Ridge National Laboratory. The SDS Group consists of researchers focused on delivering computational solutions for the study of complex engineered systems using scalable architecture-aware algorithms
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in advanced machine learning algorithms. This position resides in the Manufacturing Systems Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
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and numerical algorithms for modeling and simulation of nuclear systems. Computational Nuclear Engineers within the RTHPCM group will work with group, section, and division members and external
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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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: 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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researchers Grant writing experience Experience with high performance computing (HPC) and deep learning frameworks like PyTorch Experience developing parallel algorithms and scalable workflows for HPC resources
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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