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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar
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. The objective of this PhD project is to develop learning-based methods for maritime tracking and prediction in time- and safety-critical applications. Artificial intelligence techniques will be utilized in order
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machine learning and AI applications toward achieving zero emission vessels under this initiative. The project consortium consists of SINTEF Digital, SINTEF Ocean, Wärtsilä-Finland, Wärtsilä-Voyage Germany
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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
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consortium, 23 partners across Europe, aims to unlock the hidden potential of global metagenomic sequence space using a combination of synthetic biology, machine learning (ML), and ultrahigh-throughput
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on maritime digitalization supported by machine learning and AI applications toward achieving zero emission vessels under this initiative. The project consortium consists of SINTEF Digital, SINTEF Ocean
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synthetic biology, machine learning (ML), and ultrahigh-throughput screening (microfluidics) to discover new enzymes and bioactive molecules with applications in biotechnology, medicine, and sustainability