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. Integrate clinical, electrophysiological, and sleep-related datasets to generate meaningful research insights. Data Analysis & Interpretation Analyze complex neurophysiological data derived from SEEG
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orbitrap high resolution mass spectrometry. Other duties will depend on the Scholar’s prior experience but may include sample analyses, data analysis, or supervising and mentoring students. Work Performed
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, Physiology, Biology, or a related field. Experience conducting independent research in neuroscience or a related discipline. Demonstrated experience with experimental design, data analysis, and scientific
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RNA sequencing (scRNA-seq) data analysis. Previous hands-on experience with cell culture techniques. Experience designing and conducting independent scientific research. Strong analytical, problem
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management. Experience with statistical analysis of data and geospatial analysis tools (e.g. ArcGIS). To apply : Submit a cover letter, curriculum vitae, and the names and email addresses of three references
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(including doctoral research) in an analytical chemistry laboratory. The ideal candidate will have strong skills in mass spectrometry (coupled with HPLC), data analysis, ability to handle multiple priorities
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, epigenomics, single-cell technologies, stem cell biology, muscle biology, aging, immunology, gene and cell therapy, computational analysis, or animal models are especially encouraged to apply. Strong motivation
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, genomics, or computational analysis of high-dimensional datasets. Experience with one or more of the following: Flow cytometry Single-cell sequencing Spatial transcriptomics Multiplex imaging Bioinformatics
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, and/or surgical procedures. Data analysis and quantitative methods using R, Python, MATLAB, or similar platforms. Miniscope imaging acquisition and analysis. Ex vivo imaging of nervous system tissues
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related fields.MD/PhD with molecular biology research experience. Experience/Training: Must have experience with analyzing omics data. Familiarity or direct experience with analysis of 10x Genomics data