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for innovation. The successful candidate will work with a variety of AI and machine learning frameworks, including open-source libraries (e.g., Hugging Face, LangChain, spaCy), cloud AI platforms (e.g., AWS
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fluorescence data. Developing machine learning methods to optimize data collection. In addition, the project is committed to developing open source tools that benefit the imaging community. The applicant will
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graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk assessment
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-quality education and in-demand skills. We are seeking a temporary salaried Auto Body Instructor who is a dynamic team-oriented individual to instruct within the Autobody Programs. The candidate will be a
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both conventional and modern machine learning approaches/frameworks. Experience in engagement/marketing activities, and/or in the development and management of effective research partnerships and
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, Indigenization, internationalization, student wellness, undergraduate competencies, and other teaching and learning priorities. Your general location of work will be remote, with the use of your own reliable
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the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning algorithms, integrate multi-modal
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Description: About us: The University is a global leader in research and teaching. We provide diverse and extensive areas of study organized around intimate learning communities. That is what creates
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to fairness and ethics issues surrounding machine learning. An applied approach will be taken, where students get hands-on exposure to the covered techniques through the use of state-of-the-art machine learning
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recovery. Oversee the collection, processing, and analysis of physiological data from recreational and elite athletes across various exercise protocols. Utilizing machine learning and deep learning