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Field
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applications for a postdoctoral or more senior researcher to conduct spatial quantitative research on social vulnerability to hazards. The researcher will have the opportunity to work on multiple projects
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intelligence, AI-based data analysis, transcriptomics, proteomics, genomics or spatial omics. Interdisciplinary background or interest. A focus on Wet Lab research development. Candidates with experience
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required to produce maps and perform simple spatial analysis for other members of the project. They will be involved in publications, with opportunities as both a first and co-author, and will also engage in
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intelligence, AI-based data analysis, transcriptomics, proteomics, genomics or spatial omics. Interdisciplinary background or interest. A focus on Wet Lab research development. Candidates with experience
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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development. Key Responsibilities: - Develop and implement spatial analyses of environmental exposures - Create and validate neighborhood typologies - Analyze associations between environmental factors and
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storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties. You will
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of techniques for countering information maneuvers, differences in online harms and influence campaigns across countries, geo-spatial differences in online harms. Successful applicants will need experience in
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far; strong gravitational lensing). Under the supervision of Prof. Martin Bureau, the postholder will primarily work with the international WISDOM team on high spatial resolution (~10 pc) ALMA studies
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and clinical outcome through comprehensive tumor profiling. The project integrates spatial transcriptomics, long-read sequencing, DNA methylation analysis, and microbiome profiling to gain a deeper