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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
generation; 2) vascular biology and vascular aging; 3) tissue regeneration and repair; 4) bioengineering or chemical engineering; or 5) single-cell RNA/ATAC sequencing analysis and spatial transcriptomics
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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a cluster hire across two research areas: (1) Environmental Social Science, Education and Communication/Meaning-Making and (2) Environmental Data Science and Spatial Computing. This job listing is for
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advanced contextual spatial and temporal modelling approaches. You will work on combining multi-source data such as field observations, laboratory measurements, environmental data, and drone- and satellite
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advanced contextual spatial and temporal modelling approaches. You will work on combining multi-source data such as field observations, laboratory measurements, environmental data, and drone- and satellite
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the Department of Geography at the University of Florida. This research focuses on quantifying the spatial variability of seasonal-to-interannual variations in total water levels along the U.S. Gulf and East
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a cluster hire across two research areas: (1) Environmental Social Science, Education and Communication/Meaning-Making and (2) Environmental Data Science and Spatial Computing. This job listing is for
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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able to analyse complicated data. Good statistical knowledge and experience with geo-spatial analysis techniques is a benefit. Prior knowledge on wetland ecosystem functioning, and particularly on carbon
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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