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Field
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statistical and spatial analysis. Skills in conceptualization, design, and production of complex data visualizations to clearly communicate analytic findings. Strong critical thinking, analytical, problem
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such as comparing spatial locations across time, highlighting changes in various dataset components, and integrating ancillary information layers within a fully three-dimensional (3D-in-3D) workspace
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transcriptomics and genomics datasets. The goal is to identify molecular and spatial signatures of disease progression both in human samples and in experimental models of diabetes. Qualifications: PhD in
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contributing to specifically the area of handling spatial data to assess the distribution of several soil properties and fungal communities using samples collected from multiple habitats and land use types at a
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blockade (Phillips, Matusiak, et al, Nature Communications, 2021). We do research at the forefront of spatial biology and offer training in immunology, human histology, statistics, computer vision, grant
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soil microbial communities. You will be contributing to specifically the area of handling microbial data to assess the spatial distribution of several soil properties and fungal communities using samples
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contributing to specifically the area of handling spatial data to assess the distribution of several soil properties and fungal communities using samples collected from multiple habitats and land use types at a
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required Experience analyzing wearable device data including accelerometer and GPS data, as well as experience with spatial data analysis Demonstrated publication record in high quality peer-reviewed
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, mathematical statistics, or operations research. The PhD degree should normally have been awarded no more than three years prior to the application deadline (according to the current agreement with the Swedish
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Minimum Qualifications - PhD in ecology, remote sensing, environmental sciences, or a related field - Strong quantitative skills, including ecological data analysis, statistical analysis and data management