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that shape aging trajectories. The project aims to analyze omics data (e.g., genomics, epigenomics, metabolomics, and proteomics) with Mendelian randomization and machine learning methods with longitudinal
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resolution by integrating plasmonic nanopores with a high-speed Raman detection system, an automated control system, computer simulations, and advanced Raman-based bioinformatics. The RamanProSeq consortium
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communication Demonstrated track record in scientific writing and publishing We also appreciate the following know-how and experience in: Experience from machine and deep learning data analysis Experience from
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