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decisions focusing mainly on interpretable machine learning and its applications. The candidate must be an expert in music generation and Schenkerian analysis. The candidate will be responsible for working
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Bioinformatics expertise required for scRNAseq analysis. · Previous cell culture experience. · Perform molecular, cellular, biochemical and immunological analyses. · Optimize and troubleshoot experimental
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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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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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. Engage in a spectrum of research activities including literature review, experimental design and execution, data analysis, manuscript preparation, communications with collaborators and journal editors, and
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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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, United States of America [map ] Subject Areas: Climate Science Atmospheric Sciences Quantitative Analysis Appl Deadline: (posted 2025/05/12, listed until 2025/06/23) Position Description: Apply Today is the last day you can
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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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mice HLA-EVL9 antibody biology in PLWH or SHIV infected monkeys Other experiments submitted in your K99 grant Analysis of new HLA-E-VL9 antibodies from the Azoitei Lab Select studies as the budget allows
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career, with background in psychology, behavioral pharmacology, and/or neuroscience. A strong quantitative background and/or experience with fMRI data analysis is preferred. Duke offers a highly