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analytics and implementation of algorithms in care settings along with clinical, business and ethical challenges will be explored. In addition, an overview of the issues within the health industry
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candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks include
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candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks include
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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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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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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