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data and spatial-omics data collected from state-of-the-art microfluidic lymph node on-a-chip systems. The postdoc will be co-mentored by an interdisciplinary team of biologists and mathematicians
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-seq, ATAC-seq, spatial transcriptomics, …) into modeling and develop efficient data/training pipelines Drive application cases with Helmholtz Munich, MDC Berlin, and NVIDIA—for example, disease modeling
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, or related disciplines. Proven expertise in climate change adaptation, food security, and rural development. Strong background in statistical and spatial analysis tools (SPSS, MAXQDA, AMOS, LISREL, PLS, GIS
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saliva, serum, dried blood spots, and fingerprints; apply these methods to clinical samples derived from King’s biobanks and collaborators; Perform statistical analyses to identify biomarkers of clinical
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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 biodiversity and model the potential future biodiversity recovery given during land use transformation and restoration in Denmark. This involves spatial and temporal optimisation and prioritisation of land for
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candidate(s) will have knowledge and enthusiasm for waterfowl and experience with spatial and statistical models and coding to address the research objectives using modern statistical packages, open access
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, statistics, data science, applied math and/or other quantitative backgrounds who are enthusiastic about bringing their expertise to address fundamental problems in biology and medicine using cutting-edge
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of root-microbe interactions in forming stable soil organic matter in different soil types and under future climate scenarios using a range of different approaches, and in collaboration with Statistics
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advanced spatial statistical analyses related to environmental health risks (hotspot analyses, spatial regression analyses) Written or verbal proficiency in Hindi, Urdu, Bengali or another common South Asian