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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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completion) in computer science, electrical engineering, AI, machine learning, remote sensing, robotics, or a closely related discipline. Demonstrated expertise and research track record in deep learning and
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relentless focus on delivering high quality teaching and learning. We are a culturally rich university with strong commitment to the public good, providing a stimulating and inclusive environment where staff
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 3 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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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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holder must have: Expertise in advanced theoretical and computational electromagnetics, including high frequency asymptotics. Expertise in artificial intelligence and machine learning. Recent research
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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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of the Department. The research activities of the department fall under eight broad headings, though there is much overlap in practice: Information Engineering (Robotics, Computer Vision and Machine Learning
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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Earth Engine, ENVI, MATLAB, or R. Desirable Proficiency in applying machine learning methods to multispectral and hyperspectral data for detecting crop diseases and estimating crop yield and quality