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Field
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varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents. NYUAD is committed to upholding a culture of non
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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from varied sources, and machine learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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FieldBiological sciencesEducation LevelPhD or equivalent Specific Requirements PhD in Physics, Computational Biology, Bioinformatics, or equivalent. Knowledge of biophysics, statistical physics, 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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100 languages. Together, NYU's campuses in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 9 hours ago
on the principle that by integrating high-resolution Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk
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research. This exciting position offers a unique opportunity to develop cutting-edge Knowledge Guided Machine Learning (KGML) methods informed by ecosystem modeling of agroecosystem carbon and nitrogen