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posted for informational purposes only. At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 month ago
Date Posted: 08/04/2026 Req ID:49692 Faculty/Division: UofT Scarborough Department: UTSC:Ctr-Teaching & Learning Campus: University of Toronto Scarborough (UTSC) Existing Vacancy: Yes Description
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with the Learning & Leadership Centre and other HR stakeholders, the position translates identified capability needs into appropriate learning and development approaches and supports their implementation
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evaluate evidence-informed simulation-based learning across programs within the School of Nursing. The SBL Instructional Support (IS) faculty member will be integral in the development of Interprofessional
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and global society, provide a transformational undergraduate learning experience, pave the way for game-changing startups and new industries, drive breakthroughs in health care, and create the knowledge
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MGCR 271 GEOG 216 AEMA 101 AEMA 202 AEPH 112 AEBI 120 As a PALs Peer Leader, you will be responsible for facilitating a weekly peer-assisted learning session that gives participants an opportunity
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on the Posting End Date. Job End Date September 10, 2027 At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning
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Adjunct - Non-Approved Program / On-site apply for this job Continuing Education and Professional Studies (CEPS) at SAIT offers a wide range of programs designed to support lifelong learning, career
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productive work and learning environment. Engages in continuous learning by participating in activities, training, and events related to Indigenous engagement, reconciliation, equity, diversity, and inclusion
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term