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Field
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cytometry, and microscopy are encouraged to apply. Additionally, previous experience analyzing transcriptomic data is an advantage, as the dry lab (e.g., single cell RNA-sequencing dataset analysis) informs
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-based perturbations, base/prime editing, and targeted epigenetic modulation). Functional genomics in neuronal systems, including transcriptomic and epigenomic profiling. Model systems such as human iPSC
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microbial genome sequencing, metagenomics, metabolomics, transcriptomics, single-cell or spatial profiling, and computational analysis. These technologies will be used primarily to identify microbial
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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evolutionarily conserved structured RNAs and candidate mammalian riboswitches from large genomics and transcriptomics datasets. The position will involve close collaboration with experimental scientists who will
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. Preferred: Integration of wet lab findings with computational analysis of multi-omic data (e.g., structural variants, transcriptomics) using R or Python pipelines. Mentoring and Professional Development
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Research Associate will design and perform infection and time-course experiments, generate and analyze host and bacterial transcriptomic data, and integrate phenotypic outcomes with differential gene
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responses, and regional molecular changes. The project will combine detailed human neuropathology with RT-QuIC seeding assays (performed at the Kraus lab), biochemical characterization, transcriptomic
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of transcriptomic, metabolomic, proteomic changes in rat/mouse models of Dementia. The individual will be responsible for planning and executing experiments independently. The individual has to be highly skilled in
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opportunities to use approaches including GWAS, Mendelian randomization, transcriptomics and multi-omics analyses alongside functional genetic studies in Drosophila. Particular areas of interest include