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Field
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computing skills to handle datasets of whole transcriptome RNA sequencing, ATAC sequencing, and whole genome sequencing. The objective of this position is to complete data analysis and integration of ATAC and
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new method to study human pancreatic tissue slices using high-resolution calcium imaging in combination with spatial transcriptomics. The researcher will develop methods to improve the viability
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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
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disease. Experience in, cardiomyopathy, cellular senescence, cilia biology, PKD, and/or bioinformatics, particularly single-cell and spatial transcriptomics, is desirable but not required. The ideal
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of prostate cancer, particularly spatial transcriptomics and single cell genomics to support and advance basic and clinical research. In this role, you will be part of the mission to share our resources and
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a combination of large-scale phenotyping screens followed by transcriptomics, phenomics and metabolomics studies with constructed holobionts. The Research Associate will work closely with
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; cloning; microdissection, especially of insect female reproductive organs; statistical analyses; single-cell RNA sequencing and transcriptome analysis; genetic mapping including QTL, GWAS, or PhyloG2P
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-based spatial transcriptomics are preferred. Candidates with at least 2 years experience in generating and analyzing single-cell RNA-Seq data from both primary retinal tissue and retinal organoids
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bioinformatics applications in cancer research Utilize advanced computational and bioinformatics approaches to analyze genomic, transcriptomic, proteomic, and other large-scale biological datasets. Publish
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, transcriptomic, or phenotypic datasets. Collaborate with the Research Software Engineering (RSE) team to support the curation, independent verification, and presentation of findings through web-based interactive