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Field
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Foundations of Artificial Intelligence Applications should hold a PhD in Computer Science, Electrical and Computer Engineering, Applied Mathematics, or related disciplines and must demonstrate research
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Interdisciplinary Training in Mathematics for Human Health,” supported by the National Science Foundation. RTG themes include modeling in cancer biology, computational neuroscience, and mathematical epidemiology
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Computational Neuroscience ● Mathematical/Theoretical Foundations of Artificial Intelligence Applications should hold a PhD in Computer Science, Electrical and Computer Engineering, Applied Mathematics
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. Qualifications • PhD degree in computational biology, bioinformatics, mathematics, physics, engineering, computer science, or a related field. • Computational background in areas such as AI/deep learning
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Computational Neuroscience ● Mathematical/Theoretical Foundations of Artificial Intelligence Applications should hold a PhD in Computer Science, Electrical and Computer Engineering, Applied Mathematics
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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education in research specialization with advanced knowledge of equipment, procedures and analysis methods. Strong background in computer programming, mathematical modeling is required. Preferred Education
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; contributing to (or leading) technical reports or manuscripts prepared for submission to peer-reviewed journals; and mentoring graduate students. Qualifications: PhD in mathematics or a closely related field
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via