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Field
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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well
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). What We’re Looking For: DVM and/or PhD (or equivalent) in veterinary, animal, or biological sciences. Experience or strong interest in: Udder health and animal welfare Digital learning and employee
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participation has transformed STEM learning environments and workforces by asking fundamental questions about how well teaching practices, curricula, classrooms, student success cultures, workplaces, and career
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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to learn more about what the Bangor region has to offer. Qualifications: Required: Doctoral degree in engineering, environmental science, geoscience, or related field. Knowledge and experience in engineering
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research university that provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students to be
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for the development and implementation of innovative statistical and machine learning approaches for analysis of genetics and genomics data. We are particularly interested in applicants who have experience with methods
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data-intensive operations in scientific and AI applications. Investigate machine learning techniques to inform heuristic methods for routing optimization, bridging theoretical insights with practical
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric