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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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of research and academic teaching, contributing to a vibrant environment of research groups focusing on ethnography, materiality, urban/spatial studies, and multimodal/audiovisual research established
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maintain algorithms in biomedical image analysis pipelines. Validate algorithm performance on novel datasets. Analyze spatial transcriptomic data. Perform statistical analyses on data extracted form bio
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research and academic teaching, contributing to a vibrant environment of research groups focusing on ethnography, materiality, urban/spatial studies, and multimodal/audiovisual research established
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to create analysis‑ready, topology‑correct 3D city models from multi‑modal geospatial data. The position is hosted at the Department of Mathematical Sciences at Chalmers University of Technology and the
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proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques. Additionally, the postdoctoral researcher will contribute to transforming mining liabilities into valuable
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⸻ Preferred / Additional Experience Experience in one or more of the following areas will be considered a strong asset: • Flow cytometry (FACS) • RNA-seq / DNA-seq workflows • Single-cell omics • Spatial
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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twice weekly. Bard CEP’s GIS course provides students with the fundamentals of using spatial information, conducting spatial analysis, and producing high-quality cartographic products, including storymaps
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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