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Field
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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and
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of extracellular vesicles (EVs) in modulating immune responses. Key Responsibilities: Design and execute experiments using a variety of techniques, including flow cytometry, CyTOF, spatial proteomics, cell culture
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. Develops advanced analytical models and systems and provides solutions and analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and safeguards
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particular, the research project will focus on inferring trajectories from spatial transcriptomics data modelling at the same time the cells evolution in gene expression and in space. Required skills : We
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genomic pathology approaches, including CRISPR, single cell sequencing, spatial transcriptomics, and image analysis to address biologically- and clinically-driven questions. Our overall aim is to
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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research, including such topics as microclimate, watershed management and modeling, sediment analysis, soil formation, water scarcity and security, geomorphology, land cover change, application of remote
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–biodiversity relationships are linked to acoustic comfort–restoration outcomes. The models will integrate spatially-explicit structural complexity variables, landscape imperviousness variables, biodiversity
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renewable energy, sustainable manufacturing, and human health. Our group specializes in dynamical and spatial control of engineered metabolisms using techniques such as optogenetics, subcellular engineering
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling