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develop integrated energy-material scenarios up to the global level and the necessary models. On this basis, potential resource bottlenecks can be identified at an early stage and model-based strategies
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–Nano Interfaces at the earliest possible date. The position offers the opportunity to develop and apply advanced methodologies for investigating interactions at bio–nano interfaces, with particular
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project partners · Very good language skills in English and German Tasks · Independent work on a research project, including method development for spatially resolved strain characterization
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Oettingen in Bayern, Bayern | Germany | 3 months ago
research, thereby developing new strategies for personalized oncology in prevention, diagnosis, and treatment of cancer. DKTK conducts transformative translational cancer research, educates the next
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Oettingen in Bayern, Bayern | Germany | 3 months ago
research, thereby developing new strategies for personalized oncology in prevention, diagnosis, and treatment of cancer. DKTK conducts transformative translational cancer research, educates the next
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interactions and social evolution. ● Prior experience with microbiology and insect experiments (desired but not required). ● Proficiency in biostatistics (R) or a strong motivation to develop quantitative and
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exchange and professional development in an interdisciplinary environment through annual symposia, workshops, and retreats, as well as monthly seminars. Further information is available at: https://www.uni
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The position is based in the internationally renowned Functional Materials Department at TU Darmstadt, Germany which focuses on the development of resource-efficient functional materials. Your tasks
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Public Programming Contribute to CBCW research program in one of two ways, contingent upon experience: Development of concepts for building mutual connections between individual research project and other
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models (LLMs) have become significantly more difficult to detect, as a poisoned parameter update is harder to recognise than a poisoned dataset. Therefore, several methods have been developed recently to