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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 month ago
- Population Genetics Course Description: This course introduces students to the genetic variation between and within populations. The topics include evolutionary forces, quantitative genetics, and Bayesian
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. Effective oral and written communication skills. Ability to exercise tact, discretion, and judgment required. Strong analytical skills, including the ability to analyze numerical data, draw logical inferences
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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Studies Course description: The use of proxy data (terrestrial and aquatic microfossils) to infer past environmental conditions. The nature and extent of Quaternary environmental change is considered in
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expertise in quantitative data analysis not necessary but appreciated) in quantitative research, • Prior experience in working with health administrative databases, causal inference methods, biostatistics
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the least squares linear regression model in depth and may introduce models for discrete dependent variables as well as the maximum-likelihood approach to statistical inference. Emphasis on the assumptions
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Sessional Lecturer, INF2205H - Designing Sustainable & Resilient Machine Learning Systems with MLOps
Description: Decision-makers in modern organizations rely on machine learning systems to infer insights from information by analyzing meaningful patterns in the connections and associations within data. Leaders
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quantitative research, particularly methods for longitudinal analyses, causal inference, and natural experiments (e.g. difference in differences, interrupted time series) Demonstrated experience (as demonstrated
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of inference and reconstruction in archaeological interpretation. August 27 – December 3, 2025: Tuesday & Thursday (8:35 - 9:55 am) Leacock 617 Teaching Qualification Requirements: Education : MA required
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, and anti-racism, • Experience working with historically marginalised populations, • Prior experience in working with health administrative databases, causal inference methods, biostatistics