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Field
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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple
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, and visualization are preferred. Prior training in longitudinal data analysis, survival analysis, Bayesian methods, and joint modeling is highly desirable. Experience working with clinical or biomedical
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Masters project Supervisors Login Recently added Development of a GIS-Based Model for Active Citizenry Street-Level Environment Recognition On Moving Resource-Constrained Devices Bayesian Generative AI (PhD
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The Department of Statistics employs about 15 researchers, teachers, doctoral students, and other staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods
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collection, pipeline automation, data visualization, and advanced statistical analysis. Candidates must have experience managing large-scale survey datasets, multilevel bayesian modeling, game development, and
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revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit priors on the latent variables. Having a clear
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit
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Bayesian belief networks; Experience in scenario development approaches, e.g. SSPs; Experience in the application of R-based analytical tools for qualitative or semi-quantitative modelling, incl. RQDA
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in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods