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Field
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germplasm characterization, including calculation of sequence based genomic prediction and genomic selection values. To learn more about the USDA ARS NPGS and Plant Genetic Resources, visit our webpages
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investigating the functional and regulatory dynamics of ghost introgression, combining whole-genome sequencing, allele-specific expression analyses, and DNA methylation profiling across managed Red Wolves
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candidate will analyze whole genome sequencing datasets from a North American woodpecker hybrid zone to characterize patterns of introgression and selection, and connect genomic variation to phenotypic and
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-derived cells and single-cell perturb sequencing to establish foundational models to predict the effects of potential drug candidates on cardiovascular diseases. By combining genome engineering, functional
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and pathogen-plant interactions. The candidate will utilize conventional microbiology methods, genome sequencing, comparative genomics, and bacterial genetics and functional genomics tools
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leukemia using primary human samples, cell lines, and mouse models. Responsibilities will be to design, perform, and analyze data from genomic assays including long read sequencing using the Oxford Nanopore
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state-of-the-art genomics methods to understand how organisms develop. The approaches used will blend sequencing, imaging, computation, and theory. The ideal candidate will be comfortable working
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of computational biology, protein language models, structural biology, and immunology. 🎯 Main Missions Build and curate large-scale antigen sequence datasets for training and fine-tuning protein language models
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Perform NGS library preparations, such as whole-genome and whole-exome sequencing, 10x RNA-seq, etc. Perform cell culture procedures, including culture of primary cells, flow cytometry, single cell analysis
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. Experience with statistical and bioinformatics software, including R, SAS, sequence analysis tools, and genomic data analysis methods. Experience developing Excel macros and using database management systems