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, measurement, and evaluation strategy, with a focus on cross-sector data infrastructure, linkage, performance measurement, evaluation, and continuous learning. Responsibilities: 1. Serve as a scientific partner
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characterizing healthy and JDM patient-derived muscle models and evaluating the effects of type I interferons on mitochondrial function and muscle physiology. What You'll Do: Lead and execute a translational
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and evaluation of pathogen-selective, enzyme-activated carrier chemistry and will work closely with computational collaborators and other team members on iterative design-synthesize-test workflows
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. Create scalable approaches to measure shared visual attention, caregiver positioning, speech, and dyadic engagement using video, audio, pose, and object information. Design, implement, and evaluate
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endpoints of overall survival using data from cancer clinical trials and patient registries. Develop and evaluate prognostic models for clinical outcomes in patients with cancer. Conduct meta-analyses using
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project on critical metals from unconventional resources. The project goals are to evaluate the geochemical characteristics of potential feedstocks and develop extraction methods for resources such as rare
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recordings, intracranial stimulation studies, and sleep evaluations. Apply quantitative and analytical approaches to interpret multimodal research data. Utilize programming and data analysis tools such as
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infectiousness, treatment and prevention interventions, and realistic behavioral processes to evaluate strategies for reducing HIV transmission. The Postdoctoral Associate will train under the primary mentorship
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position is appropriate for a candidate who is enthusiastic and willing to undertake multiple molecular biology and computational approaches to evaluate host immunity elicited by candidate HIV vaccines when
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evaluation of machine learning, computer vision, and other algorithms, primarily in the context of health. They will be part of the thriving research community of Duke Spark (spark.duke.edu) where AI