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Field
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and Bayesian Theory and Data Analysis. We are looking for an associate able collectively to cover the different modules on the programmes, mainly around AI and Data Science, as well as supporting others
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effects model. What will you be doing? The post holder will take the lead in developing and testing Bayesian joint models for relating height and body composition growth features to early life exposures and
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probability, likelihoods and Bayesian analysis. We are also seeking individuals with a strong interest in public health. Key Responsibilities: Develop models that integrate different data types (e.g., serology
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probability, likelihoods and Bayesian analysis. We are also seeking individuals with a strong interest in public health. Key Responsibilities: Develop models that integrate different data types (e.g., serology
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or higher and two (2) years of data science or analytics experience or education and work experience to equal six years. Experience with Bayesian statistics and censored datasets preferred. Background
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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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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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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 2 months ago
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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, 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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redefinition of behavioral features or pose challenges in their detection. The projects To address these challenges, we propose developing a Bayesian Program Synthesis (BPS) methodology for generating synthetic