Sort by
Refine Your Search
-
learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
-
(EPIB 507) Fall 2025 EPIB 507 Biostatistics for Health Sciences (3 credits) Fall 2025 Epidemiology and Biostatistics: Basic principles of statistical inference applicable to clinical, epidemiologic, and
-
. 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
-
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 23 days 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
-
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
-
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
-
, and anti-racism, • Experience working with historically marginalised populations, • Prior experience in working with health administrative databases, causal inference methods, biostatistics
-
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
-
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
-
experience Minimum five (5) years recent and related experience Experience in applied statistics (multivariate models and distributions, jump processes, inference), e.g. simulation tools such as Monte Carlo