88 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" Fellowship positions at Zintellect in United States
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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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regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
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pathology whole slide image analysis are encouraged to apply. Additional preferred skills: Python programming Pathology whole slide image analysis Machine learning, especially generative adversarial networks
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to evaluate autonomous surgical robotics, mixed reality medical applications, and phantom-based alternatives. This project provides extensive learning and development opportunities across three critical areas
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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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, 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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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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seeds. This research will help to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning
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the devastating disease avian coccidiosis. The secondary goal is to compare various Eimeria spp. to identify genes involved in intestinal cell specificity, virulence, and markers of drug resistance. Learning