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of spatial transcriptomics data and the integration of imaging modalities with transcriptomics. The researcher will also contribute to the computational analysis of cell-free RNA datasets in diabetes
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the MIDA – Methods for Image Data Analysis – research group at the Department of Information Technology, and will be conducted alongside other researchers at the Centre for Image Analysis who develop
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. For more information, please see: https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in this unique program
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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(e.g., Snakemake, Nextflow) and reproducible data processing pipelines. Knowledge of transcriptomics and alternative splicing analysis, including isoform-level quantification tools. Programming skills in
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medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will
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experience in population genomic modeling (e.g., using SLiM), analysis of structral variants from long‑read data, population genomic analysis of whole‑genome re-sequencing data are a merit — these techniques