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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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: 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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Qualifications: Master of Science in Molecular Biology and Genetics or an acceptable equivalent combination of education and experience Minimum one (1) year of experience with biometric software analysis
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, confidentiality in the use of samples, autopsy ethics, ethics of clinical teaching, emerging reproductive technologies, gene editing, genetic alteration/genetic enhancement, and the ethical implications of changes
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are particularly interested in applicants employing cutting-edge approaches such as genetic and epigenetic analysis of aging, regenerative biology using organoids or model systems, senescence-targeted therapeutics