11 assistant-professor-computer-science-and-data-"St"-"St" Fellowship positions at Carnegie Mellon University
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer vision, with specialization in one or more of the following areas
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of Physics, Computer Science, Machine Learning, and Statistics, and enjoys close relationships with the University of Pittsburgh’s Department of Physics and Astronomy. Carnegie Mellon’s physics faculty hold
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analyzing data, including periodical/literature search and utilizing specialized skills in related field to analyze the collected data. Conducting research experiments within the predetermined research scope
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and data collection, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. The purpose of this role is to acquire the professional skills needed
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or microworlds, conduct laboratory studies, and construct computational cognitive models including paradigms in Cognitive Science (Instance-Based Learning models) or AI (Reinforcement Learning). The fellow will
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from multiple disciplines and institutions. Required: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer
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to combine the practical and the theoretical, the Robotics Institute has diversified its efforts and approaches to robotics science while retaining its original goal of realizing the potential of the robotics