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, supervised machine learning, live Epic/FHIR implementations for silent deployment, and multi-site data coordination. https://reporter.nih.gov/search/ljiYqBbnJkOn3jp2EpXY6g/project-details/10720073#description
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affect cancer development. The successful candidate will design and conduct statistical analyses using large datasets like EPIC and UK Biobank. This includes studying circulating proteins, proteomic
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Systems Transformation with Novel Foods based on alternative proteins (EPIC-SHIFT), we seek a proactive candidate with a strong background in generating experimental data and integrating with mathematical
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and