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Field
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
) statistical analysis of rainfall extreme characteristics (types, frequency and intensity), and associated changes in clouds, moisture and the large scale circulation, (2) analysis and modeling of extreme
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with high-performance computing (HPC) environment, cloud-based computation, software tools for genetic epidemiologic analyses (e.g. GWAS, whole-genome sequencing, polygenic score, Mendelian Randomization
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with metabolomics workflows (LC-MS, GC-MS) and integrative multi-omics analysis. Knowledge of statistical modeling and systems biology approaches. Experience with HPC or cloud computing environments
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dependable large-scale software systems, integrating expertise in: Software Engineering Machine Learning & MLOps Robotics & Cyber-Physical Systems Cloud & HPC ecosystems Interdisciplinary research. As a
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work
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particle physics, computer science or a related field Experience with large-scale data management using grid or cloud technologies Experience in one of the on-site experiment would be an asset Familiarity
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of secondary organic aerosols (SOA), aerosol-cloud interactions, anthropogenic-biogenic interactions and representation of aerosols in climate models. The group is part of the Atmosphere and Climate Competence
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computing and cloud-based infrastructure. A state-of-the-art UW Fiber Lab for DAS data and Pacific Northwest Seismic Network specialists in multi-sensor networks An working environment with a commitment to
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data