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learning with advanced algorithms such as Alphafold3 for molecule processing and foundation models for image processing. Designs and develops machine learning computer models (i.e. algorithms) for medical
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developing image analysis and machine learning algorithms and tools for aerial imaging and analysis. You will also contribute to data collection, data curation, and the development of a data portal for project
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Sessional Lecturer - AMS402H1F: Interfacing Cultures: AI, Platforms, and Algorithmic Politics Across
algorithmic power, with case studies on cross-border AI development, digital identity politics, and state-platform relations. Drawing from American Studies, Science and Technology Studies (STS), and digital
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the research functions in Data Analytics and Intelligent Systems. The Research Associate will be responsible for the following duties: Developing the research strategy and plan of the Urban Data Lake. Planning
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career. Job Summary full-time position is open immediately for a computational biologist to work with our interdisciplinary team of researchers and software developers at BC Cancer. The successful
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Title: Introduction to Computer Science Course Number: COMP 251 - Course Title: Algorithms and Data Structures Course Number: COMP 273 - Course Title: Introduction to Computer Systems Course Number: COMP
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bio-resource engineering. This position strengthens the research functions in Data Analytics and Intelligent Systems. The Research Associate will be responsible for the following duties: Developing
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managed, and how health research and discovery is conducted in the coming years. Recent focus on the application of artificial intelligence to health and medicine has primarily been on the development
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, sometimes from multiple jurisdictions, to achieve sample sizes appropriate for training algorithms. This creates challenges with data security and data flows (due to legislative restrictions). Further, data
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: Mathematics and Statistics Department Position Summary: Conduct a research project on methodological development of contrastive learning approaches for dimension reduction techniques (e.g., t-SNE, UMAP). Review