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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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operations in the following areas: Soil & Groundwater; Deactivation & Decommissioning; Tank Waste; Robotics; Machine Learning; Artificial Intelligence; Cybersecurity; and Advanced Manufacturing. Are you
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interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine their antibiotic resistance. Your work will
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in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning environments and immersion in other
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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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. Candidates are expected to have experience in one or more of the following areas, such as: wireless networks/communications, machine learning/vision, intelligent transportation systems, intelligent sensing
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
Posting Description POSTDOCTORAL ASSOCIATE, Mechanical Engineering, to perform cutting-edge research on projects centered around machine learning models for Computer Aided Design and Engineering
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work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning skills to bear as you develop new methods
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design or experimental methods and machine learning. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour 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