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complex societal issues for a sustainable, healthy and just future. From forest fires to big data, from obesity to malnutrition, from helium to the universe and from genetics to medicine: our education and
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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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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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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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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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immediate manager will be the Head of Discipline. About the project This PhD project focuses on advancing the modelling, simulation, and execution of complex cybersecurity scenarios within the Norwegian Cyber
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biological data are increasingly common in modern biomedical research but remain challenging to analyse because of their scale, heterogeneity, and complex spatial and functional dependencies. Existing methods
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project focuses on advancing the modelling, simulation, and execution of complex cybersecurity scenarios within the Norwegian Cyber Range (NCR). The research aims to investigate how cascading cyber effects
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, you will: - conduct independent research combining field investigations and numerical modelling of multimodal data (taking into account subsurface structural complexity and anisotropy); - develop