650 machine-learning-"https:"-"https:"-"https:"-"UCL"-"UCL"-"UCL" Postdoctoral positions in United States
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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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contribute to the Lab’s broader effort in conversion and separation of carbon-based materials. The role will require the individual to work with personnel that perform machine learning and molecular
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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simulation models used in rare-event searches, develop machine learning methods for background rejection, and place constraints on new physics using LZ datasets. In addition, the postdoc will lead the
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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), supernovae, gravitational wave sources, magnetic activity in exoplanets, and machine learning classification. The candidate will lead and participate in observations and data analysis of these sources in all
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to 8 hours/day, lifting <30 lbs, may view computer up to 8 hours/day. Shift Generally Monday – Friday, 8a-5p with some flexibility Job Summary This position is designed to have 70% clinical time, 15
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental