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and programs; (7) teach 1 to 2 AI-focused courses; (8) assist faculty seeking to integrate AI into their own courses; (9) assist with writing of AI-related grants; and (10) collaborate
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a postdoctoral scholar in computational biology. The PI, Dr. Lixing Yang is an Associate Professor at the Ben May Department for Cancer Research and the Department of Human Genetics. To learn more
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the global health scenario and domestically for dissemination, and plenty of opportunities for career advancement. •Learn background/research methods of studies for which analysis is conducted with limited
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research skills, contribute to high-impact projects, and work closely with the PI in a collaborative and supportive environment. Under the direct supervision of Dr. Haojun Yang , the incumbent will perform
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with strong expertise in machine learning for cyber-physical systems and a solid understanding of electric power distribution systems, and microgrid operations. The selected candidate will develop and
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collaborate closely with the PIs, student workers, and a Youth Advisory Group (YAG). This is an ideal position for a scholar with interests at the intersection of games, education, and creative computing who
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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for you. We are looking for an ambitious, collaborative and structured person to join our team. In this role, you will take a leading position in advancing the material modelling of brain tissue. Your
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/College University Research Department Institute for Modeling Collaboration & Innovation (IMCI) Location Moscow Posting Context Statement Position Overview The Postdoctoral Research Associate selected
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
our group at the intersection of statistical methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data