219 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Zintellect
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including the unique nature of clinical medical data with low disease
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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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degradation that could occur to C-130 crew members from extended exposure to environmental insults. This research will also inform the development of effective human-machine systems and healthy Airmen protocol
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of traumatic extremity injuries and amputations with a specific focus on translating their findings into clinical practice to improve the care of injured Service Members and Veterans. To learn more, visit: https
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, Computer Vision, or related field. Application Requirements A complete application consists of: Zintellect Profile Educational and Employment History Essay Questions (goals, experiences, and skills relevant
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, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills that will support advancing your scientific
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on the blockchain. A hands-on familiarity with machine learning and blockchain or related research is required as are Python or other coding skills. Quantum computing – This research is exploratory, applying hands
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research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human