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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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testing, propensity score methods, meta-analysis, Bayesian inference, and a wide range of regression models (linear, logistic, Poisson, negative binomial, lognormal, Cox, mixed-effects, GEE, penalized
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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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assessment models using approaches such as Bayesian networks and system dynamics, leveraging domain expertise and statistical tools (e.g., SPSS, Vensim) to model cyber-physical risk scenarios in the maritime
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.) experience with statistical methods (Bayesian statistics, machine learning etc.) We offer Lund University is a public authority which means that employees get particular benefits, generous annual leave and an
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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
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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