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. Based on these insights, you will formulate design rules to predict optimal loading conditions and release mechanisms, supporting experimental optimization. We expect you to be able to work with a high
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Engineering, and related fields, with a view to develop and optimize 3D bioprinted models, cell selection and expansion for bone models, biological and mechanical characterization of bioprinted tissues
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research projects following established protocols and contribute to experimental optimization. Perform advanced laboratory techniques, including RT-PCR, histology, mRNA extraction, chromatography, and
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with experts in climate modeling, geosciences and in renewable energy development have been set-up to respond to these societal issues. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre
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closely linked to a related opening at IMD-1 at Forschungszentrum Jülich within the same project consortium: https://www.fz-juelich.de/en/careers/jobs/2025-361 Please feel free to apply for both available
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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collaboration with ETH Zurich (Switzerland). Within this project, we are seeking a full-time postdoctoral researcher who will play a key role in the development, optimization, and fundamental understanding
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. The studied autoinducers include derivatives of acyl-homoserine lactones and 2-heptyl-3-hydroxy-4-quinolones. Candidates should have skills for: Optimization of molecularly imprinted polymer (MIP) synthesis
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
professionalism, enhance learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our
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by evolving different strategies to optimize interactions with their microbial environments to maximize fitness. Our long-term goal is to investigate fundamental mechanisms of defense optimization