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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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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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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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faculty and over 50,000 alumni. Through innovations in engineering education and research, we prepare the next generation of global engineering leaders to address the world’s most pressing challenges
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’ histories, materials, structures, texts, and accretions over time through the application of technologies and methods developed in the natural, computational, conservation and other sciences. Examples might
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. Responsibilities As part of the Network Architecture Lab (NAL), the Research Associate will: Design and develop orchestration, analytics, and machine learning algorithms for heterogeneous resource management
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will be expected to independently carry out research projects including formulating problems, developing machine learning algorithms, identifying/compiling/working with carbon mineralization data from