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Field
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hydro-climate studies. Strong background with GIS tools and spatial analysis techniques. Demonstrated expertise in climate variability assessment and the use of climate models. Experience with
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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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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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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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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
for high-resolution (e.g. gigapixel) imaging, or high-dimensional statistical approaches for analyzing spatial transcriptomic data. This role involves close collaboration with an interdisciplinary team
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cytometry Perform genomic data analyses from next generation sequencing data (eg. RNAseq, scRNAseq, spatial ‘omics profiling)—prior experience with genomic data analysis is helpful but not required Stay
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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