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Field
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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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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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statistical analyses on large datasets to provide biological insights. Integrates mass spectrometry imaging data with other multimodal imaging techniques (e.g., spatial transcriptomics) for comprehensive
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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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The Center for Policy Research on Energy and the Environment at Princeton University invites applications for a postdoctoral or more senior researcher to conduct spatial quantitative research
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and strengthen societal capacity to address the climate and biodiversity emergencies. The project will generate spatial-temporal datasets from various sources including Spaceborne Satellites, Drones, In
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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emergencies. The project will generate spatial-temporal datasets from various sources including Spaceborne Satellites, Drones, In-situ instruments, Land-Management, Soil & GIS databases that can be processed
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tools, data science techniques, and spatial analysis to leverage big data from cities and model their evolution. Main Tasks and Responsibilities: Collect, clean, and structure large volumes of urban