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Field
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Proficiency with HPC and Cloud Computing environments, including distributed training (e.g. torchrun, slurm, deepspeed, etc.) Excellent communication and teamwork skills PREFERRED QUALIFICATIONS: Familiarity
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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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sequencing data, whole-genome alignments, and assemblies is preferred. Candidates should be familiar with computational biology techniques and scientific programming. Prior experience with cloud and/or high
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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 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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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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access and outcomes in rural Minnesota and American Indian/Alaska Native (AI/AN) communities. In partnership with CentraCare, the regional campus in St. Cloud offers a wide range of patient experiences
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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
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are preferred. The Department of Computational Biology provides access to high-performance computing clusters, a cloud computing environment, innovative visualization tools, highly automated analytical pipelines
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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