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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
Demonstrated research experience in machine learning, deep learning, medical image analysis, computer vision, biomedical data science, or a closely related area. Strong programming skills in Python and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 13 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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Employer https://main.hercjobs.org/jobs/22419032/postdoctoral-researcher-lab-scientist-deep-phenotyping Return to Search Results
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with the Center for Avian Population Studies (CAPS, https://www.birds.cornell.edu/home/center-for-avian-population-studies/ ), one of six programmatic units at the Cornell Lab. CAPS is the home of
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working experiences, and professional mentorship and support. The postdoctoral associate will be affiliated with the Center for Avian Population Studies (CAPS, https://www.birds.cornell.edu/home/center
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research career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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research Experience with machine learning, deep learning, or multimodal data analysis Experience conducting human-subject research and IRB-compliant studies Experience working with healthcare systems
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biology to characterize carcinogen DNA damage and molecular signatures in the oral mucosa of smokeless tobacco users. This unique project will build on our deep understanding of tobacco-induced cancer