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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
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methods for code generation, code transformation, software updates, and platform-specific software adaptation Automation of automotive DevOps and DevSecOps workflows, including continuous integration
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there is an update. Candidates whose application is not compliant with the requirements above will not be considered. More information can be found from the E-Sailors project web page – https://sisu.ut.ee
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2025 for updated information on 2026 HDS PhD programme admissions. The application period typically runs from mid-January through the end of February. The 2026 HDS PhD cohort will begin their studies
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on our website (www.ec2-big-nse.de ) for updates on new PhD positions, which are expected to start in 2026. Course organisation The main characteristic of BIG-NSE is a comprehensive integration and
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Programme duration 6 semesters Beginning Other Application deadline Application track I (joint call): updated information for the next call will be announced here in due time. Application track II (open
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for students is available in the brochure "Jobben" (part-time jobs) published by the German National Association for Student Affairs. FAU’s job portal is also updated regularly with new advertisements for part