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Field
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openness to new perspectives Desired qualifications Experience with ChIP-seq, ATAC-seq/scATAC-seq, RNA-seq/scRNA-seq, WGS, WGBS, or Hi-C data analysis. Experience in computational genome studies (e.g., TF
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, retroviral gene delivery, chemical biology, drug development, whole exome sequencing and RNA-seq-analysis of clonal evolution of cancer, dot blots, RNA modification-seq, ChIP-seq, RIP-seq, PAR-CLIP-seq, single
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employing a combination of genetics, molecular biology, including Ribo-Seq, RNA pull-down and mass spectrometry, reporter assays in cells and tissues, and computational sequence analyses. This implies to work
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. Hands-on experience in performing primary human- and cancer- cell culture, complex cell-based assay development and screening technologies (including multicolor flow cytometry, RNA-seq, library
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relevant subject (cardiovascular medicine, physiology, molecular biology, etc.); Omics data acquisition and analysis (any of the following: RNA-seq, proteomics, spatial omics, metabolomics); Strong record
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vivo experiments. Desired Qualifications* Working knowledge of basic bioinformatics analysis (e.g., RNA-seq analysis, public dataset mining, pathway analysis) is preferred. A strong collaborative mindset
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in-house high-throughput and Big Data facilities for RNA-seq, scRNA-seq, microarray analysis, and bioinformatics including high-performance computation. You will participate in the research project
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
involving physiological, biochemical, and molecular experiments and the use of numerous techniques such as RNA-seq, data mining of DNA and protein sequence databases, analysis of gene function via CRISPR/Cas
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datasets, including RNA-seq, ATAC-seq, ChIP-seq, and single-cell or single-nucleus sequencing data. · Present research findings at laboratory meetings, scientific conferences, and national
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, including models customized for fungal genomes. Training will include crop and fungal RNA-seq analysis; integration of multi-omic and phenotypic data; protein-interaction and structure-based analyses; and