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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 1 day ago
appointments in the graduate departments of Cell and Systems Biology , or Ecology and Evolutionary Biology . At UTM we are committed to fostering an environment of diversity and inclusion. With a highly diverse
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: Course number and title: MIE1624F/S – Introduction to Data Science and Analytics Course description: The objective of the course is to learn analytical models and overview quantitative algorithms
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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
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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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, Python, or C/C++, with the ability to develop custom scripts and algorithms for data analysis and modeling. Familiarity with rheological characterization techniques, such as rheometry or viscometry
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machine learning algorithms. It also serves as a foundation for more advanced ML courses. The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and
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edge AI for localized knowledge preservation; AI governance and data sovereignty in digital heritage institutions and collections; study and design of recommendation systems and ranking algorithms used
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promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks
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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and
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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and