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geography with a focus on natural hazard research as well as spatial statistics in the studies BSc Geography and MSc Geography: Global Change and Regional Participation in research, teaching and
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, spatial and temporal image data trustworthy AI for sensitive and distributed data, including privacy-preserving and federated learning, uncertainty quantification, and efficient real-time inference in
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sequencing and omics datasets, including: Microbiome (16S rRNA and shotgun metagenomics) Bulk and single-cell RNA sequencing Spatial transcriptomics Proteomics Metabolomics Multi-omics integration analyses
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technologies. Key areas of research include the spatial and temporal diffusion of protest, the impact of censorship on political attitudes, public perceptions of human rights and security, and the strategic use
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, and validate machine learning and statistical models for health risk prediction; Analyse heterogeneous health-related datasets, including clinical, longitudinal, environmental, demographic
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Environmental Sciences, Ecology, Climatology or an equivalent discipline, obtained more than one year ago, with previous experience in remote sensing, satellite image analysis, the analysis of spatial trends and
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disease (Braun, Cancer Cell, 2021) in kidney cancer. The ideal candidate will have a strong background in computer/data science (including statistics), knowledge of R and python, and experience with high
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relevant discipline, e.g., environmental statistics, environmental economics, ecology, etc., and demonstrated strength in applied data science with environmental applications. Expertise in R and Python
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the Department of Ecology and Evolutionary Biology at the University of Michigan. Research in the lab is focused on the geography of evolution, and particularly on developing statistical methods for spatial
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emphasis on emerging wearable optically pumped magnetometer (OPM) technology. These methods and hardware will improve the spatial resolution, robustness, and computational foundations of MEG source imaging