33 machine-learning-"https:" "https:" "https:" "Computer Vision Center" Postdoctoral positions
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 minute ago
analytic models of infectious disease and will use dynamic models and/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and
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- Epigenomics, Machine Learning & 3D Genome Biology Employer NYU Langone Health, Skok Lab Location New York City, New York (US) Salary Postdoc: $70,000+ annually, plus benefits; Senior Computational Biologist
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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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, 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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. Our research integrates multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the
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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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settings, as well as machine learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building
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Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi-Site Real-World Data: https://www.fda.gov/about-fda/oncology-center-excellence/cardioonco-ai-ai-empowered-cardiotoxicity-risk