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future. This post-doctoral position will investigate the use of knowledge graphs on automatically extracted metadata at a cross-disciplinary global scale. Using LLMs and traditional data engineering
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. Develop and optimize genome and epigenome editing workflows in neuronal or neural progenitor systems. Generate, analyze, and interpret multi-omic datasets (e.g., RNA-seq, ATAC-seq, ChIP/CUT&RUN/CUT&Tag
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projects focusing on hPSC differentiation, and modeling neurodegenerative diseases. ● Standardize and optimize advanced molecular and imaging assays ● Analyze complex multi-omic or functional datasets
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– overarching goal of the research program is to define optimal cancer prevention and intervention strategies and match the right therapies to the right patients and to advance precision cancer care with real
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AI can accelerate provenance research processes, including especially the use of Large Language Model based transcription of printed and handwritten documents, and subsequent extraction of knowledge
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lipids to visualize and measure inter-organelle trafficking and metabolism of lipids in single cells. This position requires knowledge of the endomembrane system and strong skills in molecular biology and
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with IMPACT-Y leadership, Drs. Sarah Yip, Chris Pittenger and Godfrey Pearlson. This work will involve: Implementing and validating computational models of cognition and behavior (e.g., reinforcement
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, pharmacoepidemiology, biostatistics, or a closely related quantitative discipline. Working knowledge of core pharmacoepidemiological concepts, in particular confounding by indication, propensity score approaches, and
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with in-depth interviews on sensitive topics particularly welcome. Demonstrated ability to work in multidisciplinary research teams. Background knowledge of data science and/or AI technologies
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human genetics and/or statistics, including knowledge of genomewide methods, such as GWAS and post-GWAS analysis, and comfort with bioinformatics. This is highly collaborative work, so the candidate