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Field
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) Laboratory. The group develops clinically grounded artificial-intelligence and data-science methods to derive scalable digital biomarkers from echocardiography, cardiac computed tomography, electrocardiography
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) Analyze and interpret genetic and genomic data Participate in the clinical trial activities, including: Recruitment, enrollment, and retention of eligible study participants in hospital settings Design
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work independently as well as in a team. Experience in large-scale research project management and development, knowledge of statistics and big data analysis would be highly desirable. The appointee will
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data. • Work with metabolomics, proteomics, and dietary data. • Support the development of clinical trial intervention materials, including group education programming. • Implement natural
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work in close collaboration with clinicians, AI researchers, statisticians, and industrial partners in a highly interdisciplinary consortium, translating large-scale cardiovascular data into clinically
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. About the role As Postdoctoral Researcher in Big Data for Cardiovascular Population Health, you will play a key role in applying machine learning, health statistics, and large-scale medical and population
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, or digital health. Responsibilities The Postdoctoral Scholar will: Analyze large-scale datasets from cohorts and clinical trials, including longitudinal data. Develop and implement predictive models
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biologically and clinically meaningful disease subtypes, regulatory programs, and therapeutic vulnerabilities. This position provides strong mentorship, access to large clinical cohorts, and close collaboration
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, including R01 (NCI renewal recently scored 1st percentile) and R35 awards, as well as institutional funding from ALSAC. We are closely integrated into the Pediatric Cancer Dependency Map, PedDep, a large
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for the analysis of cfDNA/cfRNA sequencing data Integrate multi-omic cfDNA/cfRNA data with clinical phenotypes to identify and validate biomarkers of inflammatory disease Collaborate with clinical and translational