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Networks project. Planned research will investigate the characterization and recovery of bionanoparticles for vaccine delivery and gene therapy. Overall, 14 Doctoral Candidates will be trained at seven
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. Through academic, clinical, and industry partnerships, as well as global networks, we strive to translate biological discoveries into applications that enable the early detection of deviations from health
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builds bridges between past and future, between community and academia. Funded by the German Research Foundation (DFG), the RTG aims to reveal enmity as a processual, relational, and deeply ambivalent
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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advantage High motivation to complete the doctorate within three years Very good organizational skills Ability to show initiative and work independently Very good cooperation and communication skills and the
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extend your network. Good English skills and a will to work in a multidisciplinary topic are a must. Organisation The chemistry and life science research in the Leiden Institute of Chemistry (LIC) https
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procedures to be applied to RNPU networks in combination with memristive materials and applying the theoretical hysteron concept. Information and application Are you interested in this position? Please send
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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collection programs (e.g. R, fomr) Very good English and German skills (or willingness to acquire them) Interest in media communication We offer Part of a young and dynamic Emmy Noether Research Group Diverse