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will develop an end-to-end framework that links advanced optical spectroscopy (including laser-based methods such as Raman and complementary NIR/MIR techniques) to state estimation and autonomous
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of individual heterogeneity, estimation using machine learning methods and particular challenges present when studying infectious diseases. An overall goal of the project is to improve current practice in
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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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. Adaptive learning systems evolve through continuous updating, which introduces new challenges in reliability and assurance. Traditional engineering methods assume relatively static system behavior, whereas
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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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. The aim of Imagining Positive Energy Futures is therefore to use methods grounded in the SSH to address an acute need to diversify and make visible a range of hopeful, positive imagined futures that can
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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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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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analytical chemistry methods, in particular spectrophotometry and chromatography Documented experience with data analysis Documented experience in analyzing and interpreting omics data, such as genomics
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tunnels and field conditions Characterize root system architecture (RSA) using high-throughput, non-destructive phenotyping methods (e.g. X-ray CT, rhizotron imaging) to identify adaptive traits for drought