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that encompasses research units in Chemical Ecology, Resistance Biology and Integrated Plant Protection. Both applied and fundamental research are performed at the department, providing an excellent learning
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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large datasets, and applying AI approaches (e.g. machine learning, image segmentation, multimodal AI data integration) will be considered advantageous. Strong skills in communicating scientific results
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change. You will investigate how different types of learning infrastructures lead to capacity building and learning among the participants in existing experiments as well as in their direct context
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nitrogen deposition on a beech forest alters richness and functional diversity of understory vegetation and forest microbiomes (including both phyllosphere and rhizosphere). Can we observe a difference
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opportunity to learn, develop and apply a range of cutting-edge modeling and computational techniques. You will work in an interdisciplinary, cutting-edge, fast-paced research environment, interact with
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is to investigate localized flow and crystallization processes of PCMs in devices under different conditions with advanced 3D imaging tools like CT and NMR imaging, and to couple this with performance
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field of psychology from different perspectives (such as a biological and cognitive viewpoint). By using small-scale and student-centred methods, such as problem-based learning and project-based learning
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difference frequency generation sources. You will perform multispecies detection in demanding applications such as environmental and exposure monitoring, and in the green hydrogen industry. Put your ideas
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user