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conventional rule-based and manual inspection systems. These challenges demand innovative, intelligent solutions capable of real-time anomaly detection, adaptive threat response, and autonomous monitoring
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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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, wearable and nearable sensor data, continuous glucose monitoring data, self-reported data, and multi-omics analysis to develop predictive models for steroid hormone disorders, particularly adrenal
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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