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Field
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for data augmentation, small language models, anomaly detection using learning methods, and explainability techniques for decision-making. The research will involve designing and developing prototypes
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, but current methods are not always efficient or optimal. The process lacks an intelligent, informed approach to selecting the best grinding parameters, which can lead to inefficient maintenance actions
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methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as well as programming to test out
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collecting the samples, you will perform lab analyses to determine the total elemental composition and elemental speciation of various chemical compounds using ICP-MS based methods, as well as other chemical
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Earthquake Engineering and examines the dynamic and liquefaction behavior of Icelandic basalt sand, using laboratory test methods and advanced numerical techniques. The project is funded for three years by
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experiments and with real-world field data; - Integration of the numerical model in a digital management tool for riverine plastic mitigation strategies; - Publication of results in peer-reviewed journals and
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of Chemistry. This position is intended to begin January 1, 2026. The aim of this project is to develop and apply computational methods for statistical learning on chemical reaction network (CRNs) structures
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degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in
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meter with a non-insulating pipe wall. The research will use existing MHD direct numerical simulations to investigate the flow physics. You will also construct a prototype to test the performance using
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of turbulent pipe flows. In this project you explore novel fundamental approaches of an EM flow meter with a non-insulating pipe wall. The research will use existing MHD direct numerical simulations