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Field
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-cell (RNA+ATAC-seq), and spatial transcriptomics (RNA-seq) profiles of hematopoietic and non-hematopoietic progenitor cells in the bone marrow. We aim to characterize all cell types (see: https://doi.org
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to 1) compile a global database of existing information about physiological responses of cattle and other livestock to humid heat and 2) to use these data, coupled with spatially explicit forecasts
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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-new lab that previously made important contributions to the development of novel predictive computational tools in single cell and spatial transcriptomics. Representative publications include
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the field of Ottoman / Turkish Studies Habilitation project in historical migration research focusing on Turkey Research interests: migration history, minorities, spatial studies Professional and
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international collaborations Your profile at our group: analysis of high-throughput proteomic, (single cell) transcriptomic, digital spatial and other omics data in health and disease states multi omics data
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lead experimental efforts using advanced nanoscale optical and scanning probe techniques—including spatially and temporally resolved photoluminescence, electroluminescence, and a strong focus on
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Köhler Lab, Max Perutz Labs, Vienna BioCenter Rethinking cellular boundaries Cellular boundaries are not passive—they are interfaces that convert spatial separation into function. Membranes, nuclear
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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required Experience analyzing wearable device data including accelerometer and GPS data, as well as experience with spatial data analysis Demonstrated publication record in high quality peer-reviewed