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multimodal clinical datasets; maintain reproducible, documented analysis pipelines and code. • Draft peer-reviewed scientific articles with a high degree of autonomy (from analysis plan to submission and
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, ranging from early devotionals and Bollywood mythologicals to art house and parallel cinema masterpieces to explore key themes in the history and religion of India with a focus on 20th century developments
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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practical knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML
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knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML/AI systems
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knowledge of machine learning with large datasets Experience : Experience with cloud-based, parallel, or distributed computing environments Hands-on experience developing or deploying production ML/AI systems
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Medicine, established in 2017. The School is a nucleus for education and training, research, and innovation in biomedical engineering, creating new knowledge, new academic and training programs, and
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 month ago
in an IT or related program, or acceptable combination of equivalent experience. Minimum three years in a heterogeneous Windows, Mac OS, and Unix/Linux environment Must have diverse application
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performance computing. Topics include modelling, floating point computations, molecular dynamics, fast Fourier transforms, solving partial differential equations, parallel programming concepts, and hybrid
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an asset. Ability to work independently in a multidisciplinary research environment and manage multiple platform-development, assay-development, and collaborative projects in parallel. Strong troubleshooting