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Field
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Decision Intelligence for Supply Chain and Operations Optimization. The successful candidate will contribute to cutting-edge research at the intersection of Statistical Machine Learning and Generative
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that the electrolysers operate safely and reliably while fulfilling the intended specifications. The knowledge gained from the experiments will be used to determine the appropriate risk and reliability analysis
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established methods of microstructural analysis and mechanical testing with new schemes such as Acoustic Emission for non-destructive assessment of degradation and Machine Learning for development of predictive
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. You will examine, for example, how security of supply can be realistically mapped in a sector-coupled energy system, which political decisions, crises, or technological developments need to be taken
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modeling) Molecular ecology and genetic analysis Ecosystem forecasting Salmon life history Climate and ecosystems CoastWatch /PolarWatch Fisheries resources (including advanced survey technologies
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subprojects (SP): 1) SP1 includes desk research and case file analysis about how decisions are based on what kind of legitimations 2) SP2 unfolds how decisions are justified in-the-making via shadowing and
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research and case file analysis about how decisions are based on what kind of legitimations 2) SP2 unfolds how decisions are justified in-the-making via shadowing and narrative interviews 3) SP3 focusses
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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Centrum Wiskunde & Informatica (CWI) has a vacancy for a 4-year PhD position (m/f/x) on the subject of Reliable AI-powered Data Analysis in collaboration with the University of Amsterdam. Interested
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collaborating research group at the University of Hamburg interest beyond the pure scientific analysis of data and processes is expected as the focus of GERICS is on the provision of decision-making knowledge