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University of Southern California (USC) | Los Angeles, California | United States | about 1 month ago
conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and analyses for mainstream
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electrophysiology and sleep) and (ii) in spatial transcriptomics and high-resolution imaging. The selected candidate will work at the intersection of molecular biology and systems neuroscience, gaining a unique
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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experimental and computational tools, and are currently recruiting for the following roles: Position 1: AI and data science approaches applied to genomics and spatial omics Position 2: Experimental islet and
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cell or spatial multi-omics, cell free DNA methylation, longitudinal omics. You will benefit from access to unique datasets and expertise; top-ranked scientific environment; and superb compensation
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poorly understood. To investigate this topic, we will use whole-genome re-sequencing and integrative approaches which combine population genomics with advanced spatial modelling techniques based
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to create “A Spatial Transcriptomic Atlas of Embryo-Endometrial Crosstalk During Implantation and Human Embryo Development”. This position is funded by a new collaborative grant between the laboratories of Dr
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emissions due to the large temporal and spatial variations in CH4 emission and its underlying processes. The lack of continuous observations of CH4 turnover hinders our understanding of CH4 emission
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have expertise in geometallurgy, applied geochemistry, and geo-environmental assessment, with advanced proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques
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-culture system to study disease. By integrating single-cell multiomics, spatial transcriptomics, long-read sequencing, and high-throughput functional imaging, we aim to identify disease-relevant phenotypes