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institutes, the University of Toronto's Temerty Faculty of Medicine lies at the heart of the Toronto Academic Health Science Network and is a global leader in ground-breaking research and education, spanning
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development, business and personal networks and philanthropy. The Senior Alumni Engagement Officer is responsible for developing, implementing and executing alumni programming in consultation with the Director
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
also offer a Management and International Business program wherein our students complete an international work term as well as a study abroad term. UTSC is part of the broader tri-campus network, along
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confidentiality. Major responsibilities include managing the complex schedule of appointments and events for the Dean, bothinternal and external to the University; responding to a wide variety of in-person, email
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, solicitation and recognition experience Demonstrated experience drafting complex donor reports and communications Demonstrated project management, liaison and relationship management experience Provenability
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, organoids and OOCs), as well as enabling data-driven autonomous experimentation. Developing computational tools for the analysis of high-resolution microscopy images of complex tissue models and extracting
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‘net-zero’ economy will become the latest site of geopolitical competition between the US and China; who (countries, regions and companies) will gain and who will lose from this transition, and what
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Affiliation: University Health Network (UHN) & Sinai Health System (SHS) Campus: St. George (Downtown Toronto) Description: The Division of Respirology, Department of Medicine, University Health Network/Sinai
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Public School in YRDSB. The successful applicant must have demonstrated current knowledge grounded in existing professional networks with both Carnegie P.S. and YRDSB educational priorities and
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nonlinear, including neural networks), loss functions (squared error, cross entropy, hinge, exponential), bias and variance trade-off, ensemble methods (bagging and boosting), optimization techniques in ML