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will use a combination of scRNAseq, spatial transcriptomics, and highly-multiplexed imaging to understand how human macrophages respond to the early stages of cancer development. They will be a part of
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, particularly for low-income ratepayers. We seek a postdoctoral scholar with electricity system modeling and rate design experience and strong data analysis and management skills. A willingness to apply expertise
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare
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brain aging and cognitive decline Utilize advanced computational methods, including machine learning and AI, to analyze neuroimaging data (e.g., fMRI, EEG, or other modalities) Develop and apply models
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applied to large longitudinal data repositories of multimodal data (such as the Health and Retirement Study; HRS) to obtain detailed and accurate predictions of individual’s health trajectories through
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and analysis. Experience managing large datasets and executing data analysis in complex environments is highly valued. Required Application Materials: Application Instructions To apply, include
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omics data analysis. ●General understanding of scientific principles. Demonstrated performance to use knowledge and skills when needed. ● Demonstrated ability to apply theoretical knowledge of science
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technology and b) large-scale data collection in a diverse sample spanning over 250 schools across 30 states to answer three significant questions regarding the mechanisms of word reading difficulties such as
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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine