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of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus
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, excellent core facilities, and access to state-of-the-art analytical platforms. Principal supervisor: Prof. Thomas Elbenhardt Jensen Section for Human and Molecular Physiology, NEXS Email: TEJensen@nexs.ku.dk
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challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a
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for a curious and engaged colleague with a genuine interest in the research topic. You enjoy working analytically with quantitative data, approaching research questions thoughtfully, and learning new
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for candidates with strong analytical skills, curiosity, and enthusiasm for cryptographic research. A solid background in theoretical computer science, mathematics, or a related field will be considered an asset
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relevant analytes at very low concentrations in highly complex and interfering media such as blood, urine or saliva. In CAMINO, we will design, fabricate and evaluate electroanalytical sensor platforms
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advantage. Strong analytical skills, experimental independence, and the ability to work in interdisciplinary teams are essential, along with good scientific communication skills. The position offers hands
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materials chemistry and excellent analytical skills. Proficiency in scientific programming (e.g. in python). Excellent oral and written English language skills. Strong collaborative skills, team spirit and
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will require development of novel analytical assay approaches to link enzymology to reaction kinetics in complex reaction environments with insoluble and soluble substrates. It is a particular focus
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translational research and commercialization. You are structured and analytical, with a strong interest in healthcare innovation and market access. Experience with business development, stakeholder engagement