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Field
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household panel survey that is planned to be implemented in three different sites in Sub-Saharan Africa. We will hence mainly but not exclusively rely on quantitative data and methods. Applications
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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with the hydrogel • Data-based parameterization of equations for modeling the movement of flagellar bacteria • Calculation of transport dynamics using the non-Markovian generalized Langevin equation
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macromolecular dynamics with machine learning and experimental data, integrative structural biology, and membrane protein modeling. Our group works on the definition and implementation of strategies to study
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, in Africa and Europe. Requirements include a university degree (master's or equivalent) in archaeometry, mineralogy, or materials science, as well as the motivation to develop fundamental new data
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to work on fundamental new data relevant to archaeological research and a willingness to work in a team. A further requirement is a willingness to undertake several weeks of fieldwork (collection of raw
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train deep learning models (CNNs, GNNs) for creating 2D floor plans Prepare data: Build our data pipelines – from reading architectural drawings to preprocessing and augmentation, all the way to labeling
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impact and cascading effects of hazards, precursors of crises and their aftermath, and to identify successful mitigation strategies. While global and regional data bases (pollen assemblages, land cover
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Doctoral and Postdoctoral Researcher Positions in the Cluster of Excellence “Balance of the Microver
microbiome stability, resilience, and tipping points, using model systems and clinical data to uncover what drives changes from health to disease Our Microverse Mission: We integrate insights across ecological
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machine learning, AI, and computational data analysis Solid understanding of biological and biomedical concepts, particularly in genomics, transcriptomics, or single-cell technologies Proven experience