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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
computer package will be used. Course Enrolment (Estimated): 120 Number of Positions: 1 TA Support: 50 hrs per tutorial & per semester Sessional Dates of Appointment: July 1, 2026 – Aug 31, 2026 Class
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machine learning with single-cell genomics, spatial omics, and systems biology, supported by strong collaborations across UBC and internationally. Project Recent advances in single-cell and spatial omics
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and revise existing IMAGE machine‑learning components to optimize efficiency, scalability, and quality of results. Implement conversions of existing non‑LLM components to LLM‑based approaches where
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be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact
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of an Indigenous language, which includes using a computer. Focus is on nouns, verbs, prefixes, and suffixes, along with specific Indigenous lexicon. Posting Dates: March 04 - 10, 2026 Application Deadline: March 10
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline
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on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day. The Department of Electrical and Computer Engineering has
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that incorporate artificial intelligence and machine learning or climate change and human health are of particular interest. BWF believes that a diverse scientific workforce is essential to the process and
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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, integrating and interpreting them across modalities remains a fundamental challenge. The successful candidate will develop computational and machine-learning frameworks for multimodal neuroscience data