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leave of absence. The following qualifications and experience are required: A proven research background in structural strengthening, with focus on systems such as Fiber-Reinforced Polymers (FRP), Fabric-Reinforced
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relevant subject. Experience working with statistical data analyses and mixed models. Merit: Knowledge of forage crop management. Knowledge of statistical analyses of experiments, mixed models, and
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bioinformatics, physics, statistics, computer science, computational biology, or related fields. Experience programming in Python (or R) as well as bash/shell scripting. Experience with machine-learning and deep
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, or statistics. The fellow is expected to be proficient in written and spoken English, and able to communicate scientific results clearly. Strong merits include experience in quantitative data analysis and
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experience in these areas. Further, postdocs should have a deep interest in scientific collaboration between researchers using theoretical and empirical approaches. The six projects are: A. Modeling RNA
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oral and written proficiency in English. Strong programming ability and hands-on experience with modern cloud-native technologies. Familiarity with container orchestration (e.g., Kubernetes) and
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. Assessment criteria Theoretical knowledge and practical experience, and methodological competence in the biological stability of natural polymers against mold, fungi, and enzymatic degradation are meritorious
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to increasing CO2 and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world
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or PEEM, where relevant. Detailed description of the work tasks: Responsible for designing, planning, and performing experiments, with participation of colleagues/collaborators Responsible for designing
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Experience in teaching at undergraduate and/or advanced levels in the field is desirable. Documented expertise and working experience within at least three of the following research and educational areas is a