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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
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“Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and interpretable framework for tensor analysis. Specifically
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Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
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. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models
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) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and
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in English Candidates without a master’s degree have until 01.09.2026 to complete the final exam. Desired qualifications: Solid foundation in Bayesian statistics, empirical Bayes methods and advanced
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University of Oslo. Place of work is the Department of Biostatistics (OCBE), Domus Medica, Gaustad UiO campus, Oslo. Job description The position is connected to the project “Bayesian Enhanced Tensor