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, including literature review, experimental design, data analysis, collaboration, and dissemination of findings through conferences and publications. Apply for fellowships and awards, and provide mentorship
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-funded Duke lab hub for the Human Health Exposure Analysis Resource ) program, the Newborn Epigenetics STudy (NEST) longitudinal birth cohort, the Children’s Health and Discovery Initiative (CHDI
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governance, and/or resilience; and experience with a variety of qualitative and/or participatory methodologies, including empirical environmental justice analysis, ethnography, interviewing, surveys, focus
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statistical models to investigate gene by environment interactions and to utilize bioinformatics resources and high-dimensional –omics data to elucidate the biological significance of the statistical analysis
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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. Responsibilities/Duties: · Perform biostatistics and bioinformatics for scRNA seq analysis. · Perform molecular, cellular, biochemical and immunological analyses · Optimize and troubleshoot experimental protocols
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will involve analyzing patient data and coordinating analysis of patient samples. In addition to a copy of their resume, applicants are encouraged to submit a cover letter detailing their interests and
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have hold a doctorate n environmental science or engineering or the equivalent and have expertise in the area of surface characterization and chemical analysis of nanoparticulate phases. In particular
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, excellent communication, data processing, organizational, written and oral presentation, and problem-solving skills. Experience in animal handling and/or high throughput sequencing analysis is strongly