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the Bartesaghi Lab at Duke University to work in the development of image analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET
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, engaging with other data analysts, students, post- docs and faculty on the team Conduct comprehensive high-throughput multi-omics data analysis and epidemiological analyses; Apply biostatistics and cancer
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directions that can seed future independent positions. Work Performed Depending on candidate interests and expertise, projects may involve: Analysis of global dietary patterns using genomic approaches
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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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system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine
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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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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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-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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, 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
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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