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immunology. Potential projects include developing methods for spatially resolved protein detection in live tissues, advancing ex vivo lymphoid culture models, and testing drugs and immunotherapies in murine
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modeling of biological systems. One major effort in the lab is the µDicer platform (https://www.nature.com/articles/s41378-024-00756-8 (link is external) ; https://www.biorxiv.org/content/10.64898
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of metabolic states in retinal ganglion cells; spatial transcriptomic and correlated metabolic analysis of retinal ganglion cells; treatment of retinal ganglion cells in models of degeneration; data collection
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
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Earth system models on different temporal and spatial scales to answer key questions of global change. Doctoral candidates of the IMPRS-ESM contribute to the development and application of Earth system
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
University to support outstanding research. We will foster programs in the areas of basic, translational, mechanistic, and population research. Position Summary The Carmichael lab (https://idc9.github.io/) is
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CHS under the direction of Dr. Mingxia Gu. Our research group is at the forefront of pioneering the next generation of human-induced pluripotent stem cell (iPSC)-derived organoid models. We focus
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NIST only participates in the February and August reviews. Community Resilience Metrics The Community Resilience Program (https://www.nist.gov/community-resilience) is developing science-based
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+ benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic interactions with
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intelligence and spatial data management with a view towards modern didactics, starting as soon as possible. The professorship for Big Geospatial Data Management concentrates on the methodology of acquisition