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interdisciplinary team will develop a machine learning based monitoring system that leverages spoken language processing (SLP) and natural language processing (NLP) of speech recorded at home to calculate relapse
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today BaneNOR has a scheduled maintenance program. To reduce costs, they want to introduce non-invasive predictive analysis. Predictive maintenance and condition monitoring is a hot topic in electric
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various applications including monitoring indoor and outdoor air quality for health and environmental concerns, perishable food detection to reduce food waste, breath analysis in medical diagnosis
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calibration of Raman spectra, FT-NIR and FT-IR with focus on quantitative monitoring and prediction of selected ‘lead molecule’ concentrations good understanding of the chemical species and reactions involved
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cases, cross-case analyses, thematic inspections, and risk monitoring, and there is a need for research to support evidence-based supervision. The aim of the project is to develop new knowledge about how
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monitoring and subsea installations. The applicant for the PhD position is expected to contribute to existing and future projects with additional analyses and modelling based on collected data from
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the advances in battery technology, as it enables more precise monitoring, control, and optimization of battery performance throughout its lifecycle. By incorporating digital tools such as algorithms, and
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of three topics: 1. Combining synthetic aperture radar (SAR) images with probabilistic weather prediction models to view and predict dynamic sea ice properties. 2. Using multi-frequency SAR, coupled with in
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system stability. To address these challenges, predictive maintenance (PdM) techniques have emerged, leveraging real-time condition monitoring, fault diagnostics, and remaining useful life (RUL) estimation
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the Bergen Snow Simulator (BESSI), or combinations of the aforementioned. The results of this work will be used for assessing the impact of variable snowpack conditions on glacier retreat, the hydrological