798 algorithm-development-"Multiple"-"Simons-Foundation" "Prof" positions at University of Toronto in Canada
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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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development programs and services beneficial to student success. We aspire to proactively pursue innovation in all services, processes and clear/transparent communications and to provide an environment
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, this candidate will align computational methods with experimental workflows. The focus will be on developing advanced machine learning algorithms for monitoring various in vitro cell culture models (2D, 3D
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Preferred qualifications: PhD in History preferred. Demonstrated evidence of superior undergraduate teaching. Description of duties: Developing syllabus; course preparation; Teaching one two-hour classes
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undergraduate teaching. Description of duties: Developing syllabus; course preparation; Teaching one two-hour classes weekly, in-person lectures; Marking; Conducting scheduled office hours (1 hour/week) Duties
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preferred. Demonstrated evidence of superior undergraduate teaching. Description of duties: Developing syllabus; course preparation; Teaching one two-hour classes weekly, in-person lectures; Marking
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 21 hours ago
University of Toronto Planning involvement as well as heritage consultants; electrical service upgrades to several buildings; and code upgrades in multiple buildings. Additional responsibilities within
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possibilities. We are at the forefront of redefining pharmacy’s impact in health care and developing the capacity of pharmaceutical science to pinpoint better therapeutic targets, create new ways of building
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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