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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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at the Dynamical Systems Section is very wide ranging. From foundational research in work on statistical forecasting, modeling of spatial and temporal processes and time series analysis to applied research in wind
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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
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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours 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