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reactive transport or thermal hydrology modeling. Experience with high-performance computing (HPC). Motivated self-starter with the ability to work independently and to participate creatively in
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, classification, and interpolation of pixel detector data. Exploration of spike-based data encoding/decoding strategies. Hyperparameter optimization using advanced computing resources (e.g., HPC clusters). Detector
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systems against adversarial threats. Leveraging ORNL’s world-class high-performance computing (HPC) infrastructure—including Frontier, the world’s first exascale system—CAISER conducts end-to-end AI
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of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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Background in REST API development and database management (PostgreSQL, MySQL) Proficiency with HPC environments and job schedulers (e.g., SLURM) Experience with deep learning frameworks and neural network
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of computational scaling and efficiency for large-scale HPC environments. Strong background in materials science with an emphasis on phase transformations and mechanical behavior of structural materials. Familiarity
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version control, CI/CD, testing frameworks, configuration management, and scalable computing architectures. Familiarity with high-performance computing (HPC), data management workflows, or large-scale data
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centered on Microsoft Azure and related AI cloud technologies with High Performance Computing (HPC) for on premise AI environments and Azure, AWS, and GCP for off premise environments. Major Duties
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(HPC), or large-scale data analysis. Experience in applying AI/ML techniques to hydrological and Earth sciences. Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C
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(HPC), IT acquisition, and computational sciences research subcontracts. Expert familiarity with ORNL business systems is highly desirable. Represents the Contracts Division in providing advice and