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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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past datasets within the URBank (https://urbank.earth ) initiative. The full-time position will be for a period of 3 years, with the possibility of a 1-year extension, and based in Jena, Germany. The
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methodological foundations of medical deep learning and develop next-generation foundation model architectures for precision medicine. Self-supervised objectives like masked autoencoders and joint-embedding
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at European facilities (DESY, ILL), respectively. Your tasks will include: Synthesis: Prepare nanocrystalline and single-crystalline NiO samples suitable for electron, X-ray and neutron studies
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approaches focus almost exclusively on tumor-intrinsic markers, leaving the stromal and immune compartments largely unexplored as biomarker sources. This project aims to develop and validate a novel liquid
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Degree PhD Course location München In cooperation with Elite Network of Bavaria Teaching language German English Languages German and English Mode of study Less than 50% online Programme duration 8
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this project aims to advance. In this project, you will work with an existing high-performance code to perform hydrodynamic simulations of swimmers in complex confinements, and also help to further develop it
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. The method assesses the novelty of contributions by reasoning about their content, rather than counting citations. You will further develop the method and apply it systematically to scientific literature and
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Max Planck Institute of Animal Behavior • | Radolfzell am Bodensee, Baden W rttemberg | Germany | 2 months ago
the University of Konstanz Teaching language English Languages The entire course is taught in English. German is not required, but it is recommended to learn the German language so as to increase integration
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-based AI for multidisciplinary tumor boards. The successful candidate will develop LLM-based pipelines, semantic harmonization, and transformer-based temporal models on a large multimodal oncology dataset