387 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions in United States
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Additional Qualifications Special Instructions Application
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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 21 hours ago
experience and interest in mental health, suicide prevention, health professions training, and/or military health, as demonstrated through any or all of the following: A record of scholarly publication and
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and stress), behavior (grazing behavior halters, accelerometer-based ear tags, chute velocity measures), environmental monitoring, and pasture measures. Learning Objectives: Under the mentor's guidance
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the underlying impacts of stressors on bee health and analytical skills for understanding colony level dynamics and predicting mass colony loss events. Learning Objectives: The fellow will learn and apply
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, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement