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Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. Special Requirements: Applicants cannot have received their Ph.D. more than
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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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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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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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(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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Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. The ability to obtain and maintain a DOE Security Clearance Special
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machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. The ability to obtain
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elements methods Modern machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills