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and use multiple resources increases the complexity for students learning processes and demands for agency and cognitive efforts change. Today we have limited insight into students’ processes and
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learning will be explored as enabling technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large
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biomedical research but remain challenging to analyse because of their scale, heterogeneity, and complex spatial and functional dependencies. Existing methods often rely on restrictive assumptions
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technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large experimental spaces, and adaptive selection
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. While adaptation studies recognise the intertwined complexities of heterogeneous built environments and societal vulnerabilities, methodological frameworks for harnessing the interests of marginalised
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administrative workload, the standardization of complex assessments, and reduced professional discretion. There is therefore a need for more research examining how digitalization unfolds in practice. The PhD
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
in complex engineer-to-order shipbuilding. Realizing the full potential of AI is both a technical and an integrative challenge. It requires combining what AI excels at, such as pattern recognition and
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challenges in recent decades. While adaptation studies recognise the intertwined complexities of heterogeneous built environments and societal vulnerabilities, methodological frameworks for harnessing
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
replace or supplement traditional planning approaches, which struggles to manage the uncertainty and variability that are inherent in complex engineer-to-order shipbuilding. Realizing the full potential
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period of 3 years. The position is subject to external financing through the RCN funded project "Quantum Oscillator Networks for Optimisation and Machine Learning" (project number 358752). About the