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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning
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Posted on Fri, 06/26/2026 - 12:27 Important Info Faculty Sponsor First name: Ruben Faculty Sponsor Last Name: Colman Stanford Departments and Centers: Pediatrics, Gastroenterology, Hepatology, and
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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degree in biomedical data science, computational biology, genetics, bioinformatics, machine learning, computer science, statistics, engineering, medicine, or a related field. Strong candidates may have
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designing machine learning pipelines, building web applications or tools, and creating and maintaining visualization dashboards. Trainees should be comfortable with: · SQL, R, and Python
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, multidisciplinary environment across multiple teams, with the ability to prioritize effectively. Eager to contribute to a vibrant group of faculty, post-docs, and students coalescing around psychometric and
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Expertise in machine learning, including building and deploying prediction models Strong data science coding skills in programs and languages such as Python, R, Stata, and SQL Experience with research in