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glycosylation disorders and translate our findings into improved therapies and diagnostic biomarkers. Using patient‑derived materials, we apply cutting‑edge metabolomics and (glyco)proteomics to dissect disease
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on neonatal nutrition and health through collaboration with experts in computational biology, microbiome science, and metabolomics. The research assistant will help analyze multi-omics data, identify key
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development of deep learning tools applied to different types of genomic data, proteomics, expression data, immune profiling, metabolomics data, or general health data. You will have a proven track record of
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cultured endothelial cells and mouse models, integrated multi-omics (e.g., bulk and single-cell RNA sequencing, metabolomics, proteomics), quantitative imaging and causal inference, and targeted gene
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experiments involving genetic and pharmacological interventions in cells, as well as functional assays. The candidate will also have the opportunity to perform animal experiments, proteomics, metabolomics, and
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development of deep learning tools applied to different types of genomic data, proteomics, expression data, immune profiling, metabolomics data, or general health data. You will have a proven track record of
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, bioinformatics; Utrecht & Sheffield) High-resolution metabolomic analyses (LC-MS/MS, MALDI-timsTOF imaging; Sheffield) Computational integrative analysis of plant transcriptomic, metabolomic, and microbial
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, metabolomics. Excellent analytical, organizational, and communication skills. Ability to work both independently and as part of a collaborative team. Please submit the following documents: Cover letter outlining
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program collects longitudinal clinical data and biospecimens to carry out research looking at the immune system, proteomics, microbiome, metabolomics, and genomics at Stanford and with collaborators/ basic
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that are important for defense against pathogens by using genomics, proteomics, and metabolomics approaches. We aim to elucidate the signaling networks involved in plant immunity by analyzing the expression patterns