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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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scenarios for various urban environments; Updating and applying reaction mechanism generators describing urban air chemistry; Developing volatility estimates relevant for urban chemistry; Developing quantum
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no estimate of their correctness which severely hampers accurate estimation of the correctness of downstream analysis. In this project we will develop novel models for estimating the correctness of genome and
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estimated to cause 1.3 million deaths annually. However, drivers of the AMR crisis are still largely unexplored in population cohorts. Also, the amount of sequencing data has increased massively in the last
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of the biggest threats to human health and is estimated to cause 1.3 million deaths annually. However, drivers of the AMR crisis are still largely unexplored in population cohorts. Also, the amount of sequencing
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negotiable, preferably in autumn 2025 or in 2026. Background Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4
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for independent creative thinking Skills in computer programming and experience with Linux and possibly machine learning The appointee should either already have the right to pursue a doctoral degree at the
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Current climate change scenarios estimate the growth zones to shift approximately 80 km northwards in a decade, and the speed is likely to accelerate towards the end of the century. On the other