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for fully automated analysis of nuclear cardiology data using novel algorithms and machine learning techniques, and on the development of integrated motion-corrected analysis of positron emission
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, Artificial Intelligence, Machine Learning, Data Science/Data Analytics, or a closely related field. Candidates must have a doctoral degree conferred before the appointment date. Candidates must also be
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
, University of North Carolina at Chapel Hill ( UNC -CH). We seek a motivated candidate with a strong interest in machine learning and clinical neuroscience. The lab studies resilience and vulnerability in
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, computer science. Experience in data analysis using statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data
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, computer science. Experience in data analysis using statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
end, embedded electronics, and firmware. Develop and validate signal-processing and machine-learning algorithms that account for skin pigmentation, low perfusion, and motion. Work with UNC clinical
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advanced deep learning and machine learning algorithms Assist with organizing large-scale multimodal neuroimaging datasets, brain imaging quality control, and processing Assist with grant applications
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advanced deep learning and machine learning algorithms Assist with organizing large-scale multimodal neuroimaging datasets, brain imaging quality control, and processing Assist with grant applications
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) and the integrative modeling led by the graduate student. Apply machine-learning and AI-assisted tools in genome analysis, population and disease-ecology work, and pipeline development, with attention
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) and the integrative modeling led by the graduate student. Apply machine-learning and AI-assisted tools in genome analysis, population and disease-ecology work, and pipeline development, with attention