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Field
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complex process often requiring multiple calls to the patient, referring physician, previous facilities/providers to request records and record review with the clinical team. Independently determine
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, technologies, models, and algorithms, and provide recommendations on their implementation. Sports Science Data Specialist II Responsibilities Assist in managing technological processes, conducting real-time data
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aims to make discoveries by mapping the brain. The Lab is using high-throughput serial electron microscopy (SEM) to completely image multiple brains and nervous systems. The massive data sets will be
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mathematical approaches, algorithms and software to advance scientific research in multiple disciplines, often in collaboration with other Flatiron Centers. CCM has particularly strong research groups in
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working in the exciting and dynamic fields of deep learning and medical imaging. The main focus will be to develop novel deep learning algorithms to analyze medical imaging data (e.g., ophthalmology
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
nutrition research. Our on-site facilities include multiple wet labs and clinical facilities as well as a vivarium and a metabolic chamber. Approximately two hours from UNC-Chapel Hill , the NRI is located
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
nutrition research. Our on-site facilities include multiple wet labs and clinical facilities as well as a vivarium and a metabolic chamber. Approximately two hours from UNC-Chapel Hill , the NRI is located
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artificial intelligence. You will work with a diverse group of faculty, postdocs, and students from multiple disciplines, including cognitive science, psychology, computer science, statistics and machine
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manages multiple projects of varying complexities. Based on expert technical knowledge, skills and experience, develop broad-based solutions involving multifaceted technologies, and business processes
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procedures (e.g., multilevel modeling, longitudinal data analysis, machine learning algorithms), cleaning and structuring large datasets, validating model assumptions, and ensuring reproducibility. Synthesizes