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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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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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specialists to advance the science in GSDs. This postdoc position requires a minimum two-year commitment and will consist of a hybrid work arrangement (approximately 60% in office, 40% remote). This position
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, FACS/flow cytometry, and single cell multiomics among other assays to resolve the role of lncRNAs, such as Xist, in establishing, maintaining, and disrupting immune tolerance. The successful candidate
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this program; no new applications will be accepted after 2025/06/06 11:59PM US Eastern Time. Description The Duke University Program in Environmental Health, part of the Integrated Toxicology and Environmental
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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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analyzing single-cell and spatial profiling data using modern techniques. This work should have contributed to significant pre-prints or published manuscripts, where the candidate played a leading role
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. The position will be primarily based in the Department of Biostatistics & Bioinformatics, Duke University School of Medicine, under the supervision of Dr. Xiaofei Wang. The Postdoc Associate will also work
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Associate. Occupational Summary Responsibilities will include developing next-generation genome engineering technologies for gene and cell therapy applications as well as functional genomics. In particular