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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models
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. et al. (2024). Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization. Scientific Reports 14, 10080. https://doi.org/10.1038/s41598-024-60478-9
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and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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understanding of statistics (e.g., hypothesis testing, Bayesian statistics) Good collaborative abilities, independence, and critical thinking. Preferred qualifications In-depth experience with LLM agents
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of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions/division-of-biostatistics-and-population-health ) in the Department of Biomedical Informatics (BMI
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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Investigate the use of causal discovery methods in "concept drift" situations in structural causal models. In semiparametric Bayesian networks, investigate the selection of covariance matrices and the
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric