631 parallel-and-distributed-computing-phd positions at University of Toronto in Canada
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have a completed, or nearly completed, PhD degree in an area related to the course or a Master’s degree plus extensive professional experience in an area related to the course. Teaching experience is
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. Proficient use of Microsoft Office, spreadsheet, and databases. Advanced knowledge of learning management systems, technology-enhanced learning solutions, as well as a range of computer applications, web
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to apply; however, Canadians and permanent residents will be given priority. MINIMUM QUALIFICATIONS: Education PhD and Postdoctoral Fellowship in photocatalytic materials chemistry and photoreactor
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: This course is offered as a required course in the MT program. The instructor is expected to prepare, organize, and teach this course, be available to students seeking assistance, and manage deadlines
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: This course is offered as a required course in the MT program. The instructor is expected to prepare, organize, and teach this course, be available to students seeking assistance, and manage deadlines
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will include: Providing consultative advice on data requirements. (e.g., Reviews project data set creation plans assigned to analytic staff) Planning new releases and versions of computing operating
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Date Posted: 07/31/2025 Req ID: 44591 Faculty/Division: Faculty of Arts & Science Department: Department of Computer Science Campus: St. George (Downtown Toronto) Description: Course number and
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open only to students in the Teaching program. NOTE: This course is one of the pair of courses to be taught by the same instructor. Both courses are connected to an inquiry-based, data driven school
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development, including practical assessment strategies. This course is normally open only to students in the Teaching program. NOTE: This course is one of the pair of courses to be taught by the same instructor
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of information on public issues. The RDC offers both lecture space and a computer lab for tutorials. While the specific goal of this course is to introduce students to empirical methods for the analysis