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Identification of soil invertebrates (e.g. mites, springtails, insects) using modern and classical techniques Laboratory analyses of soil properties Statistical analysis of complex ecological datasets Presentation
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recover from disruptions, as well as the risk of cascading interactions between them. Key responsibilities: analysis of the planetary-scale resilience and stability of the Earth system (its capacity
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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or integrated pest management with hands-on experience in agricultural field trials solid knowledge of statistical analysis and publication of research results first experience in acquiring third-party funding is
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to this interdisciplinary project by investigating the oceanic component—specifically how marine environmental variability influences R-Mode signal propagation and positioning accuracy. AIR-MoPSy is a joint effort of the IOW
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phenotyping, including image analysis evaluations, for trait quantification Handle NGS datasets for RNAseq or SNP detection and linkage analysis using R Your qualifications and skills: You have a PhD or
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along different design options (reward payment paid out as loans or transfer to governments or as universal cash transfers to populations) Applied optimal taxation theory analysis to assess potential tax
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The Weierstrass Institute for Applied Analysis and Stochastics (WIAS) is an institute of the Forschungsverbund Berlin e.V. (FVB). The FVB comprises seven non-university research institutes in Berlin
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ecologist (m/f/d) with a background in community ecology, macroecology, metacommunities, and/or analysis of ecological time series. The applicant should have a strong track record of scientific publications
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, aggregation, linking and retrieval of comprehensive heterogeneous and distributed data sources. To this end, both statistical and linguistic analysis methods (NLP) as well as machine learning in combination