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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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. Responsibilities may include: Designing and conducting studies on the clinical impact of GLP-1 and other metabolic therapies Developing and applying computer vision and machine learning techniques to analyze
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning
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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
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receptor (CAR) T-cell therapies for pediatric solid tumors. The Ramakrishna laboratory focuses on optimizing CAR T-cell therapies for children with cancer by learning about the biology of these CAR T-cells