Postdoc position in Statistics at the Oslo Centre for Biostatistics and Epidemio
University of Oslo, Wind Lake, WI, United States
Organisation/Company University of Oslo Research Field Medical sciences Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 19 Apr 2026 - 23:00 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
A three-year postdoc/researcher position in statistics is available at the Oslo Centre for Biostatistics and Epidemiology (OCBE ), Department of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. 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 for unsupervised learning 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 unsupervised Integration of multi-source Data in cancer Genomics and the digital Economy (BRIDGE)”, funded in 2025 by the Research Council of Norway (RCN) under the open scheme “Researcher Project for Scientific Renewal”, and it is fully funded for 3 years. The project is conducted in close collaboration with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033.
The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and probabilistic recommender systems. Emphasis will be placed on rank-based models and their successful use in probabilistic recommender systems, but many other research topics may also be relevant for the project, such as scalability, post-Bayesian methods, and in general computational and methodological challenges in integrative unsupervised learning. Method development will be motivated by the research questions of the funded project, but the candidate will be also free and encouraged to pursue her own research agenda.
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A three-year postdoc/researcher position in statistics is available at the Oslo Centre for Biostatistics and Epidemiology (OCBE ), Department of Biostatistics, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. 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 for unsupervised learning 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 unsupervised Integration of multi-source Data in cancer Genomics and the digital Economy (BRIDGE)”, funded in 2025 by the Research Council of Norway (RCN) under the open scheme “Researcher Project for Scientific Renewal”, and it is fully funded for 3 years. The project is conducted in close collaboration with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033.
The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and probabilistic recommender systems. Emphasis will be placed on rank-based models and their successful use in probabilistic recommender systems, but many other research topics may also be relevant for the project, such as scalability, post-Bayesian methods, and in general computational and methodological challenges in integrative unsupervised learning. Method development will be motivated by the research questions of the funded project, but the candidate will be also free and encouraged to pursue her own research agenda.
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