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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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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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Experiments (DoE) and Bayesian optimization, manage research data using the NOMAD research data infrastructure, and apply data-driven optimization strategies. Analyze and interpret experimental data
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project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI, advanced programming in Python) in small classes of max. 10 participants