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engages substantially with artificial intelligence, data science, or scientific machine learning. The successful candidate will have a primary appointment in Mathematics and opportunities to collaborate
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programs Posting Summary We are seeking qualified adjunct clinical instructors to teach on a per diem basis in our continuing education program through the Center for Professional Development. We deliver a
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countries (HIC). The airm of this PhD project is to understand how E. coli colonization contributes tot he risk of diarrheal disease and infections with AMR and use this knowledge to design interventions
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challenge our students, staff and faculty to maintain such standards and we are delighted to have those join us, who can help us in our pursuits. Learn more about us by visiting our website at https
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 5 hours ago
cleaning, denoising, and prediction, including approaches based on statistical machine learning, deep learning, and Transformer architectures. Perform signal analysis in both the Fourier (frequency) domain
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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Posting Close Date Qualifications Minimum Education and Experience ● PhD in Computer Science or related discipline. ● Has a proven track record of excellence in artificial intelligence/machine learning and
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of Nursing: teach, participate in service-related activities that support the school, and engage in clinical scholarship. In addition, as an advanced practice nurse educator, the faculty member is encouraged
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, including gas turbines and combustion engines. We combine advanced computational fluid dynamics, theory, machine learning, high-performance computing, and experimental methods to investigate and model complex
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning