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-Performance Computing (HPC) LanguagesENGLISHLevelExcellent Research FieldComputer science Internal Application form(s) needed wiss._Stellenangebot_Post_Doc_Englisch_Perso.pdf English (323.1 KB - PDF) Download
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-on experience in the design, construction, commissioning, and operation of complex experimental setups Programming experience in scientific computing environments (e.g., Python, MATLAB) Independent, proactive
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, experiments, documentation and statistical analysis of results; interpretation, discussion and presentation of results Computational analyses of high dimensional data, including flow cytometry, scRNAseq and
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, contributing to a healthier society in such an inspiring workplace is truly meaningful.“ Get to know us Benefits Career Development: Postdoc program, scientific training & career center with tailored offers
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to improve transplantation outcomes and next-generation cellular immunotherapies. We Offer an internationally visible translational research program participation in innovative, high-impact research projects
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2026 - 21:59 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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magnetic states and excitations in quantum magnets. Develop methods for the analysis of large datasets and comparison with theoretical models. Perform numerical simulations to interpret experimental results
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simulation, fabrication, systematic characterization, high fidelity operation as well as integration and scaling technologies. Our ultimate vision is a fully integrated semiconductor processor with integrated
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» Public policy Environmental science » Natural resources management Geography » Human geography Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 10 Aug 2026
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. The PostDoc project is part of the “CoEvolve” research consortium, funded by the European Union through an ERC Synergy Grant. The goal of the project is to better understand and predict the co-evolution