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, and analysis of spatial data, using Geographic Information Systems and Artificial Intelligence (AI), and integrating multiple technologies and methods (for example: geographic and statistical databases
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axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate–water–energy–agriculture interactions, with applications
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
on the study of human tumors through the use of multiple genomic technologies. The methods used include gene expression profiling using bulk RNAseq and scRNAseq, DNAseq, and spatial transcriptomics. The main
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genomic approaches is to explore clinically relevant aspects of brain tumor biology. We pursue this goal using patient samples profiling, investigating temporal and intra-tumoral/spatial heterogeneity as
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communities • Multivariate statistical analysis of community and environmental datasets • Spatial analysis and georeferencing of ecological data using GIS • Development and implementation of species
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(SHRUG), and others) to map multidimensional violence and multidimensional poverty and gender. The role involves managing, harmonizing, and analysing complex survey datasets, and generating statistical and
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friendly tools for data collection in the field. You ideally have experience with: Working with large spatial datasets, such as environmental data cubes. Applying and evaluating a statistical sampling design
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on complex problems involving the development of new theories and methodologies. The research will be largely focused on the development of predictive computational tools for the analysis of the spatial spread
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11 Apr 2026 Job Information Organisation/Company Lunds universitet Department Lunds universitet Research Field Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Application
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environmental management or policy. Experience with spatial and spatiotemporal statistical models Experience with diet analyses and/or predator–prey modelling. Experience with trophic ecology methods such as