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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | 14 days ago
analytical skills (Frequentist and Bayesian statistical frameworks) Experience in collecting and/or analyzing observational data on animal behavior, including ‘focal and party follow’ studies, as well as ‘ad
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research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build accurate probability density functions (PDFs
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models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high dimensional parameter
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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to develop the statistical inference side of the project, including likelihood-free and Bayesian approaches for fitting stochastic simulators to high-dimensional single-cell data. The successful applicant will
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and climate adaptation worldwide. Your research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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statistics, or decision science, who specializes in one of the following areas: statistical learning theory, causal inference, Bayesian statistical modeling, or AI and data management. [Desirable
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handling methods (e.g., FIML, multiple imputation) suited to longitudinal attrition. (EF) Apply advanced hierarchical/multilevel models using both frequentist (lme4, glmmTMB) and Bayesian (Stan, brms, rstan
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis