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technical subjects such as programming, data science, machine learning, and algorithmic fairness is highly desirable. Candidates must have teaching experience in a degree-granting program, including lecture
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. Applicants should have expertise in the application of statistical methods in data science, machine learning, or artificial intelligence. Experience must include the preparation and delivery of course content
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research-intensive university. We seek candidates able to contribute to curricular development and to teach across our diverse professional (BI, MI, MMSt) and research (PhD) programs. Evidence of excellence
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). We seek candidates able to contribute to curricular development and to teach across our diverse professional (BI, MI, MMSt) and research (PhD) programs. Evidence of excellence in teaching will be
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that is desired: Cheminformatics and Machine Learning Demonstrated development or application of machine learning tools to address chemical problems, including but not limited to: property prediction
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disciplines. The University of Toronto requires that applicants must have earned a PhD degree in Computer Science or Physics, with a clearly demonstrated exceptional record of excellence in research, service
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that is desired: Cheminformatics and Machine Learning Demonstrated development or application of machine learning tools to address chemical problems, including but not limited to: property prediction
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. Expertise that is desired: Cheminformatics and Machine Learning Demonstrated development or application of machine learning tools to address chemical problems, including but not limited to: property
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Studies; Architecture; Museum Studies; Human-Computer Interaction or a related area, with a clearly demonstrated record of excellence in research and teaching. We seek candidates whose research and teaching
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 14 hours ago
to tackle massive data sets in health. The focus will be on advanced statistical tests in machine learning and assemble such tests by accessing and validating publicly available code in the R programming