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institute, with expertise in a wide range of fields and strong communities of knowledge. We deliver research and innovation in energy, health care, climate, the environment, society and technology. Our
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Job Advertisement HKI-32/2025 The Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI I https://www.leibniz-hki.de/en/ ) investigates the pathobiology of human
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
Hands-on experience in basic organic chemistry methods - synthesis and purification Desirable knowledge of Material science Excellent self-organizational skills, ability to show initiative and work
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sciences (e.g., Soil Sciences, Earth Sciences, Biogeochemistry, Environmental Sciences, Ecology, Biology) or related subjects and strong knowledge in the dynamics of organic matter in natural systems
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/Diploma) in the field of Biochemistry, Biology, Pharmacy or Chemistry or related field Experience in experimental laboratory work Excellent knowledge of a broad range of biochemical, cell and molecular as
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devices Knowledge of Python programming Good knowledge of materials technology, and basic knowledge of environmental assessment of materials Good written and oral English skills PLEASE NOTE: For detailed
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part of the Institutes Organisation of the Dutch Research Council (NWO) . The mission of CWI is to conduct pioneering research in mathematics and computer science, generating new knowledge in
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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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interdisciplinary team working on the development of cross-platform alignment technologies that integrate material science, process engineering and sustainability analysis to deliver scalable solutions for circular
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins