771 machine-learning-"https:"-"https:"-"https:" Postdoctoral positions in United States
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learning and benthic systems. This position offers an opportunity to contribute to cutting-edge research, particularly advancing the use of remote sensing datasets and machine learning for constructing
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Informatics (HOBI) at the University of Florida (UF) is seeking a highly motivated Postdoctoral Researcher with a strong background in biomedical informatics, artificial intelligence, machine learning, or data
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will lead and participate in observations and data analysis across the electromagnetic spectrum, or will lead work on machine learning classification of optical transients. Applicants with previous
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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evolution. ● Experience with population genetics. ● Experience developing R packages. ● Experience applying or developing machine learning tools. ● Strong scientific writing skills. Brief
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backgrounds in adjacent areas and a firm commitment to learning glycobiology approaches are also encouraged to apply. Interested candidates should include a CV, brief statement of research interests, and
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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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intelligence and machine learning across several domains of physics and astronomy. The successful candidates will develop an integrated image-processing pipeline and apply it to three distinct problems: rare