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and Bayesian methods) and population studies (nonlinear mixed-effects modeling methods) and hypothesis testing Comprehensive knowledge of the concepts underlying physiological modeling and systems
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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-based, Bayesian or matrix factorization methods for multi-omics integration. Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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exploration strategies that go beyond traditional techniques such as linear programming or deterministic solvers. You will work on cutting-edge methods including: Bayesian optimization Surrogate modeling
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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. • Experience with machine and deep learning modeling approaches and developing Bayesian models. • Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial
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statistical analyses including generalized linear model, multilevel modeling, data mining, survey methodology and Bayesian influences. (Required) Demonstrated experience working on collaborative research
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open-ended position. Applicants are invited from any area of applied statistics, including statistical or actuarial data science. Those working in actuarial science, Bayesian statistics, statistical