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emotional development, learning, school readiness, and educational achievement) and AI models of detection and intervention (e.g., machine learning, large language models, neural networks). To offer a few
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implementation of advanced statistical and machine learning models, manage large-scale geospatial and temporal datasets, and enhance data infrastructure to support research and applied projects focused on hazard
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leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary
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leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised
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, embedded systems, machine learning, data science, programming languages and databases. This position will complement these existing strengths and leverage a large body of existing opportunities
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healthy and tumor-bearing animals using machine learning and AI approaches; and (3) integration of PBPK and QSAR models with AI methods to develop AI-assisted computational approaches to support decision
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Yale Center for Brain & Mind Health (CBMH) Faculty Position: Artificial Intelligence in the Promotio
integrate, advance or develop approaches such as natural language processing, machine learning, computer vision, foundation models, large language models, and other methods. Applicants may work in one
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environments, propulsion and AI-powered systems, among others. Additional information about the Department and College is available at https://www.utep.edu/engineering/mechanical/ and https://www.utep.edu