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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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development. Key Responsibilities: - Develop and implement spatial analyses of environmental exposures - Create and validate neighborhood typologies - Analyze associations between environmental factors and
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of techniques for countering information maneuvers, differences in online harms and influence campaigns across countries, geo-spatial differences in online harms. Successful applicants will need experience in
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temperature. The successful applicant will Analyze imagery from NASA ECOSTRESS instrument on International Space Station for retrieval of surface infrared radiances and sea surface temperature at 70 m spatial
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conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and analyses for mainstream
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applicants will have:*Expertise conducting spatial and statistical analyses*Experience with scientific computer programming in R and Python*Formal training or experience applying quantitative and spatial
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intelligence, AI-based data analysis, transcriptomics, proteomics, genomics or spatial omics. Interdisciplinary background or interest. A focus on Wet Lab research development. Candidates with experience
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-culture system to study disease. By integrating single-cell multiomics, spatial transcriptomics, long-read sequencing, and high-throughput functional imaging, we aim to identify disease-relevant phenotypes
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they would design, organize, and conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and
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error propagation techniques. You have experience in spatial-temporal statistics, kriging methods, and handling autocorrelation. You are interested in environmental biomass and soil mapping and modelling