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cardiac surgery outcomes research and predictive analytics using advanced statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data
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density in regulating these processes. The insights gained from these empirical studies will be paired with computational biology and machine learning to develop predictive AI models of how cells
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multi-source datasets and develop predictive models to support crop monitoring and agricultural decision-making. 25% – Sensor Systems and Ground-Based Platform Development Contribute to the design
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and the possibility of renewal. Dr. Fonseca’s research focuses on identifying early predictive markers of dementia, understanding mechanisms of cognitive decline, and advancing inclusive approaches to
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). Recover and characterize carbonate minerals formed during freezing using mineralogical and microscopic technique. Develop and parameterize a predictive kinetic framework using time-resolved geochemical
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fragmented between disciplines. We aim to overcome these bottlenecks to accelerate the development of sustainable processes. For this purpose, this project will develop predictive models, ranging from
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integrates predictive analytics, pharmacoepidemiologic approaches, and stakeholder-engaged methods to generate actionable evidence that informs clinical practice, policy, and interventions to improve
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interprets patient-level data to support the development of AI/ML/NLP algorithms for clinical applications. Annotates medical notes and datasets to facilitate training and validation of predictive
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models, hybrid ML-physics models, and climate emulators are rapidly becoming an integral part of the prediction landscape. While these approaches can achieve remarkable predictive skill, fundamental
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and data fusion, agent-based modeling and digital twins, and predictive analysis for environmental and health applications. We are committed to recruiting and retaining highly motivated, creative