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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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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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, 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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, 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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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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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
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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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-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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. 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