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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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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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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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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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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
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AI/ML, with a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex
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Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop algorithms
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Requisition Id 15854 Overview: We are seeking a research professional with fundamental knowledge in artificial intelligence (AI) who will focus on developing and applying AI algorithms to signal
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for unstructured meshes and/or finite element methods. Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC