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change. Experience in quantitative methods, spatial analysis, or handling large datasets would be valuable, but full training will be provided in climate modelling, statistical downscaling, and health
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areas*; 2. Technical Skills - Experience in processing 3D point clouds (LiDAR or photogrammetry); Knowledge of GIS, remote sensing, or spatial analysis; 3. Research Experience (Preferred) — Previous
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. As part of BioM , the candidate will work in an interdisciplinary team of biologists, statisticians and philosophers, including one other PhD student (statistics) and two postdocs (spatial forest
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. Your team You will collaborate with GRS colleagues who have expertise in methods and tools for spatiotemporal analysis of complex land systems (including agent-based modelling), spatial data
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: Experience in method development, working with spatial data, and GIS Experience with univariate and multivariate analysis and working with large datasets Experience with working independently and organizing
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across different spatial and temporal scales, from building-level energy demand to district-scale interactions and their integration with wider energy networks. PhD Position in Hierarchical Graph Neural
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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(landscape) ecologist with a broad, interdisciplinary interest with experience in spatial analysis of interactions between land use and biodiversity. Your work includes teaching courses in environmental
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, motivated individual with experience in human osteoarchaeology, stable isotope analysis or other biomolecular methods. As a PhD candidate with us, you will earn a doctorate and gain valuable experience
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themes: development of innovative tools for single-cell and spatial analysis of lipid immunometabolism (Subprojects 1–5); molecular pathways linking lipid metabolism to immune activation and