21 machine-learning research jobs at University of Miami in united-states in United States
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literal contributions to the scientific community. CORE JOB FUNCTIONS Develops and implements advanced computational and machine learning methods for the analysis of large-scale omics data, including
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, Atmospheric and Earth Science is soliciting applications for a 2-year Post-Doctoral Associate position. ACES is seeking a machine learning expert to work on several exciting research projects from airborne and
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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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of Linux, FORTRAN, Python and C++, experience with machine learning, and experience with parallel computing and high-performance computing environments. The incumbent must have a proven publication record
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one of the following domains are highly desirable: deep learning models on natural language processing or computer vision, advanced analysis of fMRI using encoding or decoding models, computational
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project predicting the physical properties of uncultured viruses. The Luque lab has recently established an interdisciplinary method combining theoretical biophysics, mathematical modeling, machine learning
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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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surgery. ·Developing algorithms for real-time control and path planning of surgical instruments. ·Integrating machine learning and computer vision techniques for autonomous surgery. ·Collaborating with
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has expertise in the development and application of state-of-the art models of machine learning and data science to uncover hidden relations between genomic data and phenotypes in cancer and a track