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of statistics and machine learning. Modern vessels produce vast amounts of multivariate data streams. The project addresses the development of trustworthy statistical and machine learning methods for anomaly
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processes of the study systems of our collaborators. Core components of the research involve, among others, Bayesian hierarchical modelling, shrinkage methods, machine learning (ML) or dimension reduction
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at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to conduct cutting-edge research on AI-based forecasting and analytics for shipbroking and maritime decision support
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