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pathology. The successful candidate will gain expertise in stem cell biology, genome engineering, neural differentiation, and bioinformatic analysis while addressing fundamental questions about how
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. Tissue sectioning for advanced spatial transcriptomic analysis. Leading the analysis of single-cell and spatial transcriptomics data. Applying and developing the analysis framework for spatiotemporal
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Strong quantitative and analytical skills Experience in either experimental neuroscience (e.g., in vivo neurophysiological recordings, behavioral training) or computational data analysis (e.g., spike
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, biostatistics, data science, or a related field are encouraged to apply. A candidate who has recently submitted the PhD thesis or is about to submit the thesis is encouraged to apply. A strong computational
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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translational oncology, and a vibrant research community that spans discovery to clinical implementation. Specific Responsibilities include: experimental design, data acquisition, data processing, statistical
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workflows for data cleaning, linkage, and analysis within Stanford’s secure computing environment. Collaborate with multidisciplinary teams of clinicians, economists, and policy researchers. Prepare
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screening, high-content imaging, or functional assays of sensory or neuronal activity. · Computational or bioinformatics experience for analysis of omics data. Required Application Materials: 1. Cover letter
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schools, are affiliates. The institute pursues its objectives through seed funding, hosting interdisciplinary centers, organizing convenings, and leadership training. Required Qualifications: A PhD in data
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be the focus of the first year: improving lead detection of paint (ICP-OES, XRF and colorimetry) and managing the analysis of a representative survey collecting over 10,000 samples of spices, soil and