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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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Trustworthy AI Systems NSF Research Traineeship, the Digital Trade and Data Governance Hub, the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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signals, imagery, and natural language, via adaptive machine learning; 2) Personalization of sequential decision making, biophysical digital twins, and the operation of digital and physical systems; and 3
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the