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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
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a research staff of 180, of which 65 are PhD students. More information about us, please visit Department of Molecular Biosciences, The Wenner-Gren Institute Project description Proteomics-driven
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The project aims to develop large-scale self-supervised models trained on rich, longitudinal health data, including: Medical records Lifestyle data Biological samples (genomics, transcriptomics, proteomics, etc
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, proteomics, long-read sequencing). Familiarity with machine learning approaches, particularly artificial neural networks, and their application to biological data. Experience with workflow management systems