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observations and field data collection. Strong competence in data preparation/analysis/visualization with Python, R, or a similar scripting and visualization language. Experience with high performance computing
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/geospatial analysis (e.g., Python, R, GIS) skills are considered an asset. Applicants are asked to submit one bookmarked pdf file: (1) curriculum vitae; (2) a maximum 1-page letter of interest describing your
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/AI systems Familiarity with modern MLOps practices and tools for experiment tracking, pipelines, model deployment, monitoring, and reproducibility Experience with relevant technologies such as Python
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/AI systems Familiarity with modern MLOps practices and tools for experiment tracking, pipelines, model deployment, monitoring, and reproducibility Experience with relevant technologies such as Python
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Familiarity with modern MLOps practices and tools for experiment tracking, pipelines, model deployment, monitoring, and reproducibility Experience with relevant technologies such as Python, Spark, Docker
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Familiarity with modern MLOps practices and tools for experiment tracking, pipelines, model deployment, monitoring, and reproducibility Experience with relevant technologies such as Python, Spark, Docker
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, Python-based proof-of-concept. Develops and maintains robust software (primarily in Python) for DICOM tag de-identification and replacement, folder reorganization, and automated data quality control and
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orchestration systems. Support Systems: DNS, DHCP, Windows, MAC, UNIX, XML. Scripting: Python, Perl, or Shell. Routing and Switching: BGP, BGP/MPLS, policy routing, redistribution, multicast; IPv4 and IPv6
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oral and technical written communication skills. Working knowledge of programming, scripting, or markup languages such as Java, Groovy, Python, Perl, HTML, XML, and JSON is an asset. Basic working
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epidemiology and biostatistics. Advanced knowledge of R, Python, SAS or other coding language for data management and epidemiologic analyses. Advanced experience linking and working with provincial