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: PID2024-155657OB-C22 Job title: Development of compact storage for analysis-ready geospatial data platforms Research line / Scientific-technical services: Development of compact data structures
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areas: GIS software; spatial analysis and visualization; programming in R, Python, or similar; epidemiology; etiology; data communication; systems evaluation; quantitative data collection and analysis
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respond to diverse mechanical cues from their environment, including adhesion, stiffness, tension, shear, pressure, and confinement. These cues are integrated across wide spatial and temporal scales, from
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capped RNA (TLDR-seq): full-length sequencing of capped RNAs (Nucleic Acids Res 2025, doi:10.1093/nar/gkaf240 ) Spatially resolved analysis of microenvironmental gradients in cancer (Science Advances 2024
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their choice of preferred cults. The main methods used in the project include spatial analysis, predictive modelling, and the analysis of geocoded data, especially of epigraphic and archaeological origin. MAIN
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. The ideal candidate will have a solid background in quantitative methods, with particular emphasis on spatial data analysis, landscape ecology, and the integration of telemetry, demographic, and environmental
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skills. Additional Merits Experience in one or more of the following areas will be considered a strong advantage: Single cell and spatial multi-omics analysis Complex Cellular Systems/ Organoid models
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Dipartimento di Scienze Biomediche Sperimentali e Cliniche "Mario Serio" - Università degli Studi di Firenze | Italy | 10 days ago
• Stem cell analysis in cancer • 2D and 2D complex in vitro cell and organoid cultures • Spatial and Liquid biopsy processing and analysis • Cell engineering • Bioinformatics and data analysis Where
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combines spatial point process models for the emergence and arrangement of objects (including birth-death dynamics, merging, and non-overlap constraints) with methods from shape analysis, in particular
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: Quantitative analysis of experimental data and description of spatial structures in crowds (e.g., Minkowski functionals, Voronoi analyses, clustering methods) Comparison of physical structural analyses with