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Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference
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(https://www.ntnu.edu/isb/prostomics#/view/about ), which is part of the broader CIMORe environment at the Department of Circulation and Medical Imaging. The group is located at Øya Campus as part of
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when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and probabilistic recommender systems. The position is connected to the project “Bayesian Rank-based
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience