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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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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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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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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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research. Interests and/or background in Arctic research, hydrological modeling, surface water fieldwork, community engagement, and/or spatial statistics are also a plus. PROJECTED START DATE There is
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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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understanding of statistics. The ability to analyse DNA-sequencing data is required (exomes, genomes, duplex sequencing); experience analysing RNA-sequencing data (bulk, single cell, spatial), or other -omics
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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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single-cell and spatial multi-omics datasets. The primary focus of this role is to delve deeper into the molecular mechanisms driving intra-tumor heterogeneity, plasticity, and therapy resistance
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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