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Field
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific
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post-doctoral research associate position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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About the Opportunity SUMMARY The lab of Professor Albert-László Barabási is looking for Postdoctoral Research Associates in the area of network science, nutrition, biological networks, machine
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
Experience Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning, artificial intelligence, natural
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of tools and public goods. Requirements Applicants must have (or expect to have at time of appointment) a PhD in the social sciences, statistics, computer science, or related fields, and their interests must
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applications from candidates in Computer Science and Engineering, with a focus on a broad range of research specializations, including: Trustworthy AI and machine learning and its societal impacts Cybersecurity
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position. The successful candidate will be driving cutting-edge research into textile fiber recycling sorting technologies using spectroscopic and hyperspectral methods coupled to machine learning techniques
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employment is contingent upon the availability of funding. Minimum Education and Experience Ph.D in a related field. Preferred Education and Experience Preferred Education and Experience PhD in Psychology
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topics include, but are not limited to (i) developing statistical and machine learning methods for study designs and decision-making in early detection of pancreatic cancer (ii) establishing strategies