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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
learning. Within the Visual Intelligence Research Centre, the PhD positions are affiliated with the UiT Machine Learning Group . The positions are for a period of three years. The objective of the position
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is
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Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. OCBE has collaborations with leading biomedical research groups in Norway and internationally. OCBE