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sequencing instruments into microscopes! Your mission You will join our team, a leading group in the field of DNA nanotechnology specialized in creating molecular tools for life-science research (http
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metastability processes under different operating conditions (dark, illumination, electrical bias); Spatial analysis of performance inhomogeneities and degradation mechanisms using optical and electrical mapping
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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quantitative analysis of imaging performance, such as spatial resolution, phase sensitivity, and system stability. Documented ability to publish research in peer-reviewed international journals and present
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change. Experience in quantitative methods, spatial analysis, or handling large datasets would be valuable, but full training will be provided in climate modelling, statistical downscaling, and health
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, motivated individual with experience in human osteoarchaeology, stable isotope analysis or other biomolecular methods. As a PhD candidate with us, you will earn a doctorate and gain valuable experience
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University of California, San Francisco | San Francisco, California | United States | about 8 hours ago
prinicpal investigators within the Genetic Center to design and implement analysis pipelines in several broad areas of computational biology including analysis of experimentally generated datasets in basic
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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, the approach provides nanoscale, spatially resolved chemical and electronic insights crucial for advancing energy technologies. By coupling SEHI with automated data acquisition and advanced digital analysis
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aseismic deformation, background seismicity, swarms, and repeating earthquakes is key to constraining models of subduction dynamics and earthquake preparation. The PhD will build on dense seismo-geodetic