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manipulation tools (e.g., CRISPR/Cas9) is a plus. Other duties as assigned. Minimum Education and/or Work Experience PhD required Qualifications PhD Certificates/Credentials/Licenses PhD Computer Skills Office
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analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM) Experience with machine learning and artificial intelligence techniques Experience with predictive modeling Environmental, Health
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principals to problem solve work. ● Ability to maintain detailed records of experiments and outcomes. ● Ability to quickly learn and master computer programs, databases, and scientific applications
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a
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mathematics, statistics) awarded within 5 years. Strong theoretical understanding and practical experience in machine learning, foundation models, and computer vision. Strong publication record in machine
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
language processing, computer vision, medical image analysis, or a related area. Required Qualifications, Competencies, and Experience Demonstrated strong communication, teamwork, and leadership skills. Strong
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biology, super-resolution microscopy, live-cell imaging, multi-omics, and mouse disease models to understand insulin-secretion deficiency and synapse degeneration. (See https://www.mcw.edu/departments/cell
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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, Coastal Marine Science, Computer Science, Electrical Engineering, or a closely related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI