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their analytical and numerical predictions with available experimental data. Applying the developed models to provide quantitative understanding of how the spatiotemporal profile of corticoids and androgens varies
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by comparing their analytical and numerical predictions with available experimental data. Applying the developed models to provide quantitative understanding of how the spatiotemporal profile
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estimation, and learning-based prediction models that anticipate the future motion of vessels seen in the radar data, based on the radar data, local geography and historical patterns. The methods
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signatures for data association and state estimation, and learning-based prediction models that anticipate the future motion of vessels seen in the radar data, based on the radar data, local geography and
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of maritime business models. As digital solutions replace manual coordination and increase data-driven transparency, the patterns of supplier relationships, contractual arrangements, and responsibility
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maritime AI business models Apply for this job See advertisement This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is
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at foreign educational institutions Other career-promoting work can be discussed and agreed Required selection criteria You must have strong competence in modelling, control, optimization or artificial
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– and building on recent advances in foundation models, neural model predictive control, and robotic world models – this PhD project will investigate principles and mechanisms for a shared autonomy
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Fotograf Morten Hjertø 20th January 2026 Languages English English English The Department of Ocean Operations and Civil Engineering has a vacancy for a PhD Candidate in maritime AI business models
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. The objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is