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more via our website. Your next career opportunity You will lead computational analyses of single-cell and spatial transcriptomics data in abnormal uterine bleeding research. You will collaborate closely
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analyses of single-cell and spatial transcriptomics data in abnormal uterine bleeding research. You will collaborate closely with US-based clinicians and experimentalists to translate discoveries
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analysis of transcriptomic, spatial transcriptomic, genomic, and metabolomic data in cutting-edge projects exploring host–pathogen interactions. The successful candidate will join a new ERC-funded research
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and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and bioinformatics analysis for large-scale spatial
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engineering. · Gene-expression and regulatory studies. · Transcriptomic and other genomic approaches for defining downstream consequences of genetic perturbation. · Mechanistic
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transcriptomics datasets, and of paired molecular and functional measurements (e.g., electrophysiology), as well as optimally leveraging integration with existing genomics datasets. The role is focused
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, and hypothesis generation. Integrate transcriptomic, epigenomic, spatial transcriptomic, and other multi-omics datasets. Perform gene regulatory network, transcription factor, pathway, and cell-state
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early metabolomic disruptions, followed by the genetic and biochemical dissection of the mechanisms underlying these alterations. The project takes advantage of recent transcriptomic and imaging data
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Microbiomes Institute — University of South Florida | Tampa, Florida | United States | about 21 hours ago
transcriptomics, or other multi-omics approaches, with bioinformatics analysis We seek motivated scientists with strong organizational and interpersonal skills who work well both independently and as part of a team
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or are not adequate. 2.) Apply computational, statistical, and AI/ML approaches to uncover biological insights and support functional discovery by analyzing multi-omics datasets, eg., genomics, transcriptomics