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over 600 known members. These compounds pose a significant risk to food safety, yet their full characterization remains a major analytical challenge due to their chemical complexity and natural
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at the very frontiers of knowledge on climate change, Earth’s climate system and climate feedbacks. Within its 10-year research programme, funded by NWO , EMBRACER brings together a wide range of world
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participatory practices—and contribute to actionable strategies for improving how public spaces are managed in complex and changing environments. Ready to challenge existing paradigms and shape the future
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an interdisciplinary approach, encouraging collaboration between researchers from different fields to drive innovation and address complex scientific challenges. The research programme of the Feringa group focuses
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include: Designing and executing experiments with plants, microbes, insects and drought stress Analyse complex data sets involving metagenomics, transcriptomics and metabolomics Manipulate the soil
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the breeding of complex resilience traits for several crops in different growing systems. This collaborative effort involves four universities and numerous companies. It encompasses scientific research, data
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. (2) Prof. Francesca Grisoni (https://molecularmachinelearning.com/ ) leads the Molecular Machine Learning Group at the Technical University Eindhoven and will lead a project on designing potent cyclic
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vibrational Non-Destructive Testing (NDT) methodology for quality assurance in metal AM. By going beyond the state-of-the-art, we aim to optimize AM benefits, particularly for complex geometries. Insights
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mutualistic and antagonistic insect flower visitors in the greenhouse and in the field. Analyse complex data sets involving e.g. metabarcode sequencing Chemical analysis of flower nectar using HPLC and GC-MS
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experiments with herbivorous insects, plants and microbes Analysing complex data involving e.g. metabarcoding, metagenomics, and metabolomics Isolating and culturing microbes and testing inoculants on insect