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Field
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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in chemistry, physical chemistry, physics, biophysics, biology, biomechanics, mechanical engineering and electrical engineering work together in the Cluster. The HEiKA Graduate School on “Functional
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interactions through chemical and physical methods, we tailor the structural and mechanical properties of bio-based materials to advance the potential of biopolymers in various applications. Project description
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“Light- versus electron-induced spin-state switching of complexes on insulating layers” within the Priority Programme SPP 2491 “Interactive Spin-State Switching” This DFG-funded project aims
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. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods
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organised in five divisions: Biology, Chemistry, and Process Engineering; Informatics, Economics, and Society; Mechanical and Electrical Engineering; Natural and Built Environment; and Physics and Mathematics
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to probe metabolic states and function of control mechanisms. Analysis of strain performance and control mechanisms using computational tools (e.g., flux balance analysis, kinetic models, network component
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. Your tasks identify relevant satellite datasets of irrigated areas, as well as of hydrometeorological, land surface and vegetation variables apply modern statistical methods on these datasets to detect