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of the subprojects) please refer to the following link on the project website: https://dynamiken-des-religioesen.uni-frankfurt.de/ausfuehrliche-projektbeschreibung/ Your tasks: Conduct an independent dissertation
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data-intensive applications. Researchers in this field design robust, scalable architectures and pipelines to transform raw data into reliable, accessible formats, leveraging techniques like real-time
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mass spectrometry analyses, including experimental design, data acquisition, and data interpretation. This role involves developing and optimizing methodologies, assisting users from diverse research
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methodological approaches. The selection is done according to the SCRIPTS Diversity Rules & Guidelines: https://www.scripts-berlin.eu/about-us/Diversity/Diversity_Rules_Guidelines/index.html Job description: The
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from the lab to real-life applications, improving lives. The Institute
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-Sailors - Electric solar wind Sail doctors (salary according to MSCA regulation) starting May 1, 2026. This project has received funding from the European Union’s Horizon Europe research and innovation
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their collaboration and thus offer innovation-promoting research and working conditions for professors, university and non-university researchers, and students. At the IRS, the applicant will head the interdisciplinary
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modelling and the land-surface model used in the project. Develop simplified, fast-running model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient
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the optical tweezers Use and further develop simulation tools to design and benchmark control strategies before deploying them on the microscope Integrate your software with the microfluidic live-cell imaging
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from protein structures and simulations Design and training of QSPR and machine learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET