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mechanisms. The overall goal of the research project is to develop process understanding and parametrizations that lead to improved, energetically consistent, climate models. Close collaboration with the other
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scientists on, e.g.: Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and
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: 01.10.2025 Application deadline: 03.09.2025 Tasks Execution of experimental work in a mouse model of cortical multiple sclerosis Application of in vivo imaging and quantitative analysis methods Investigation
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 6 days ago
to understand the reaction mechanisms and stir the selectivity towards hydrogenated N-forms, while suppressing the formation of undesirable products Detailed modelling, simulation and optimization
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Helmholtz-Zentrum Berlin für Materialien und Energie | Berlin, Berlin | Germany | about 11 hours ago
an understanding of the underlying magnetic processes through complex data analysis and computer simulations. You work in close collaboration with theoretical physicists to test models and develop new insights. Your
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and parametrizations that lead to improved, energetically consistent, climate models. Close collaboration with the other research areas of the CRC is expected, and more information can be found
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; from data integration to data-related topics such as uncertainty quantification, model-order reduction, or multi-fidelity methods. The primary fields of application are life science, medicine and health
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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to remote sensing and simu-lation modeling. A particular focus of our work is on mountain forest ecosystems. A quantitative understanding of ecosystem dynamics provides the foundation for the development
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features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and characterizing defects such as dislocations Applying generative models (e.g., GANs