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to develop data-driven, space-time explicit precision agronomic solutions Utilizing high-performance computing (HPC) systems for large-scale geospatial data processing, model training, and validation Designing
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biology. Knowledge of machine learning, AI techniques, and statistical modeling applied to biological data. Proficiency with high-performance computing (HPC), cloud computing, or workflow automation tools
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troubleshooting operational anomalies in HPC systems is an asset. Good team player with strong focus on delivering results to meet the scientific needs. Self-motivated and demonstrated ability to work
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Nature Careers | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
pipelines, proficiency in genomic association analyses, particularly involving large-scale datasets, and familiarity with cloud computing and/or high-performance computing (HPC) environments
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pipelines, proficiency in genomic association analyses, particularly involving large-scale datasets, and familiarity with cloud computing and/or high-performance computing (HPC) environments
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computing and/or high-performance computing (HPC) environments for bioinformatics workflows. The successful candidate must exhibit excellent communication skills; a demonstrated potential for excellence in
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computing (HPC) environments for bioinformatics workflows. The successful candidate must exhibit excellent communication skills; a demonstrated potential for excellence in research productivity; a Ph.D., or
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, and familiarity with cloud computing and/or high-performance computing (HPC) environments for bioinformatics workflows. The successful candidate must exhibit excellent communication skills; a
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including the ability to work effectively with others in a team environment − Demonstrated ability to efficiently organize assignments and establish priorities − Experience in HPC and cloud computing in
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teaching supercomputing/HPC to diverse groups. The main criteria for selection are: • evidence of high-quality scholarly research expertise that complements existing research areas in the department that is