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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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frameworks to maintain secure and compliant environments. Document system architectures, processes, and best practices, and contribute to internal knowledge sharing. Participate in on-call rotations and off
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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systems. Expertise with batch schedulers (SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Proven ability to lead technical projects from concept through implementation, balancing
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challenges facing the nation. Purpose: The Advanced Engineering Technologies (AET) Group is seeking a dedicated applied mechanical engineer. This position will involve machine design and process engineering
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connected through a unified digital thread platform that captures multimodal, high-frequency data across the full manufacturing lifecycle, from process execution to post-process characterization
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connected through a unified digital thread platform that captures multimodal, high-frequency data across the full manufacturing lifecycle, from process execution to post-process characterization