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Field
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Python, for processing and interpreting complex proteomics data Familiarity with proteomics software for data analysis, visualization, and management Experience with biological samples (e.g., FFPE, plasma
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government, administration and business in their efforts to adapt to climate change. It builds up a national and international network structure in order to integrate existing competences and knowledge, and to
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science-based prototype products and services in support to government, administration and business in their efforts to adapt to climate change. It builds up a national and international network structure
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Southeast Asia, ’ led by Prof. Dr. Judith Bovensiepen. This project investigates the complex interplay between animism and extractivism in Southeast Asia. Each position involves conducting in-depth
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/ ). The candidate is expected to support Prof. Katrin Vorkamp’s research in this centre, with possibilities for networking and exchange on PFAS. However, the candidate is also expected to contribute to Prof. Katrin
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are working to advance the understanding of complex systems through the lens of networks, and to apply this knowledge to solve real-world problems in a wide range of fields, including urban science, public
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progress in human, animal health, and agriculture, but no progress for the environmental sector. The aim of this project is to fill this gap by launching a new international network — a UK-Chile
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processing approach based on flow patterning to make meter scale LCEs of complex shapes and actuation modes. ALCEMIST builds on a tight synergistic collaboration between the Experimental Soft Matter Physics
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Identification of soil invertebrates (e.g. mites, springtails, insects) using modern and classical techniques Laboratory analyses of soil properties Statistical analysis of complex ecological datasets Presentation
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to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data