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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 9 hours ago
: Course Number and Title: STA380H5S LEC101 Computational Statistics Course Description: Computational methods play a central role in modern statistics and machine learning. This course aims to give an
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learning, dynamical systems, sequence generative models and world modelling. Emphasis will be on methods that design and implement new architectures for latent dynamic modelling to enable identification
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intelligence, as it develops model learning methods based on optimization, together with predictive validation procedures designed to assess the generalization ability of models on new data. Current approaches
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 17 hours ago
estimating treatment effects in the presence of intercurrent events whose occurrence may itself carry prognostic information. Methods of interest could include survivor average causal effect (SACE) estimators
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or technical projects. Activities may include, but are not limited to, Interpreting data and analyzing results using appropriate methods, techniques, and visualizations. Practice identifying, analyzing
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use the BVA's salary calculator). Responsibilities include both disciplinary and technical leadership, as well as the implementation of new methods in official statistics. The professorship at LMU
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methods for causal inference using large-scale observational healthcare data. The project will address fundamental methodological challenges in estimating causal treatment effects from longitudinal
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innovative statistical methods. The main focus will be on methods for sample size estimation and reliability for functional data. More applied projects aimed at improving the understanding of movement data may
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. Develop capacity-estimation, demand-forecasting, performance-benchmarking, and what-if assessment methods to support planning and operational decision-making. Investigate the operational, environmental
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estimated from observed data. The overall aim of the project is to develop statistical theory, methodology, and computational methods for such complex data problems, with a particular focus on models