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resolution Utilize cutting-edge techniques including microelectrode arrays, microfluidics, animal behavior and spatial transcriptomics under infectious conditions Bridge hamster and human biology to find common
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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access to the university supercomputing services to support this work. They may also be required to produce maps and perform simple spatial analysis for other members of the project. They will be involved
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. Integrates mass spectrometry imaging data with other multimodal imaging techniques (e.g., spatial transcriptomics) for comprehensive spatial analysis. Establishes new research protocols and procedures
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storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties. You will
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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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research, with a focus on understanding the molecular mechanisms driving lymphoma progression and therapy resistance. This position will leverage cutting-edge approaches including spatial transcriptomics
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computational power and the increasing availability of large volumes of remote sensing data with finer spatial and temporal resolutions have significantly transformed the way we approach climate and weather
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for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions. -Collaborate with
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methodology for analysing long-term spatially structured data sets within a joint species distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups