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Field
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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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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 three-dimensional distribution of the phases and their structural parameters. You will optimize the spatial resolution at which the 5D ED data can be obtained. You will also help other researchers
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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
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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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background in analyzing large astronomical datasets is essential, particularly in characterizing non-isotropic distributions and spatial-kinematic properties using Gaia and/or DESI data. Proficiency in
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diversity and dynamics of metabolic states in retinal ganglion cells; spatial transcriptomic and correlated metabolic analysis of retinal ganglion cells; treatment of retinal ganglion cells in models
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, a spatially-explicit agent-based model of land use change in Great Britain. This will be coupled with a system dynamics model of macro-scale drivers of land system change. Working with colleagues from
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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