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project “ComDisp: Community-Centered Modeling of Housing-Related Health Disparities.” ComDisp develops a grassroots modeling framework to predict health disparities under different climate change scenarios
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computational modeling. The key objectives of ComDisp are: • Identifying and understanding housing, air quality, and respiratory health issues in each case study. • Linking climate change models to housing
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systems such as fuel cells and batteries. Intelligent control and management systems will be designed to enhance efficiency, sustainability, and seamless integration. Developed models and strategies will be
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extinction is affected by changes in the environment such as expected alterations of the climate and loss of critical habitat types. The primary model system for examining the validity of the models will be a
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of environmental modulation on gametic and early developmental epigenome and epitranscriptome. The project uses next- and third-generation sequencing technologies to generate the data. Zebrafish is a model organism
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electron microscopy analysis, Raman spectroscopy, fluid inclusion analysis, potentially appropriate petrochronological methods, and 3D geological modelling. The project will be conducted in partnership with
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/latest-news/2020/new-research-and-innovation-center-to-provide-climate-friendly-value-creation-on-the-norwegian-continental-shelf/ Cement is the traditional material used in the well construction phase and
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computational skills (using R, modelling software, working on a remote linux-based server) and experience in analyzing Next Generation Sequencing data, including PCA, outlier analysis, GO-term enrichment analysis
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archaeological excavations and dating with climate modelling on the one hand and research on human minds and sociality on the other. The PhD position will be part of an interdisciplinary project with the goal
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years ago. Its main goal is to unravel where, when, and why early humans started to think and behave the way we do today, by combining archaeological excavations and dating with climate modelling