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on the analysis of complex event history data, with some relevant topics being the analysis of outcomes under competing risks, studies of individual heterogeneity, estimation using machine learning methods and
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plant research and methods including growth analysis and gas exchange measurements Knowledge and/or interest in whole plant physiology (from soil to plant) Good computational skills and a working
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the development of methods or frameworks to support secure and trusted data sharing for maritime AI model development. Depending on the candidate’s background and the project’s progression, the research
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, employing multivariate analysis (MVA) and advanced data-driven approaches, including artificial intelligence (AI) methods. The material mapping results will be validated using complementary analytical
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to an expected tipping point in robotics, where robots graduate from mere tools to teammates. The PhD candidate is expected to apply methods in human-centred design (HCD) to one or more real cases of embodied
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animal trials Comprehensive understanding of modern dairy production Documented experience with data handling and statistical methods Documented experience with animal behavior analyses or laboratory
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circular, nature-based method for removing PFAS from soil, water, and food systems. We combine three innovative steps: Phytoremediation: Plants absorb soluble PFAS from contaminated soil. Pyrolysis
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lifecycle. Adaptive learning systems evolve through continuous updating, which introduces new challenges in reliability and assurance. Traditional engineering methods assume relatively static system behavior
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key enabler of the energy transition, it also introduces complex stability challenges, many of which are not yet fully understood or addressed by conventional methods. Existing commercial simulation
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to design and evaluate Artificial Intelligent (AI) systems with and for people, aligning AI technologies with human needs and values, situated at the intersection of AI and Human-Computer Interaction