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addresses the development of trustworthy statistical and machine learning methods for anomaly detection in such streaming data (time series), potentially extended to spatio‑temporal settings. The emphasis is
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. Experience with interdisciplinary research, bridging the gap between academic methodological development and industrial applications. Relevant experience with data from the maritime sector. The University
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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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-of-the art in animal breeding, human genomics, ecology and evolutionary biology. The post-doc will thus work with a cross-disciplinary team of researchers and can contribute towards the development of methods