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liabilities of cancer stem cells Develop computational approaches for integrating metabolomics, transcriptomics, and genomics datasets across development and disease Candidates must hold a Ph.D. and/or M.D
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RNA or regulatory genomics. · Structural variation. · Transcriptomics or other sequencing-based approaches. · Molecular cloning and biochemical assays. Expertise
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(drug response, apoptosis, DNA damage, etc.). - Familiarity with NGS-based genomics or transcriptomics (variant calling, RNA-seq, single-cell or bulk) and integration of multi-omics data is highly
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, transcriptomics, proteomics, etc.). The research of the successful candidate should align with one of these research lines to further consolidate it within the research group and to support PhD
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novel in vivo approaches. You will develop the readouts that make these screens informative, from mutational signatures to morphology and transcriptomics, and the analyses needed to generate mechanistic
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, spatial proteomics/transcriptomics, proteomics, flow sorting, and immunohistochemistry among others. In addition, the application and integration of stat-of-the-art genomic, spatial, and proteomic
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clinical outcomes (e.g., therapy response), and network-based analysis to identify biomarkers. Experience with one or more multi-omic data types (e.g. RNA-seq, ATAC-seq, spatial transcriptomics, single
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processes using pharmacologic and genetic approaches Develop AI/ML tools for integrating metabolomics, transcriptomics, and genomics datasets across development and disease Candidates must hold a Ph.D
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Alzheimer's disease. Apply and optimize advanced spatial biology techniques, such as spatial transcriptomics, proteomics, and multiplexed imaging, to analyze brain tissue. Lead data analysis and
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and spatial omics technologies, including transcriptomics, metabolomics, or multi-omics data integration. Proficiency in programming languages and analytical tools such as R, Python, and machine