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focused on digital tools and methods for urban planning and decision-making. Develop and apply computational urban models, simulations, and data-driven frameworks to support urban policy and planning
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cross-regional logistics network scenarios, data collection and development of decision support models for optimal maritime infrastructure for collection of the captured CO2 from the emitters and
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and other marine species. The PhD candidate is expected to contribute to ongoing and future projects with measurements, including analyses and mathematical modelling of already collected and new data
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. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic deformations and wetting-like processes at cellular membranes. The theoretical
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of Carbon Capture and Usage in the climate transition, for instance through modelling. Third, studies of CCU relevant policy developments at the national level, for instance explored through process-tracing
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. The research topic of this PhD is Accurate and Scalable Simulation of Edge Systems.You will be part of the Cyber-Physical Systems group . General information about the position. The position is for a period of
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systems and models to enhance learning through AI technology. The Postdoc fellow will engage with developing models, frameworks and technologies that facilitates more efficient development of rich
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transfer function Apply machine learning methods to identify optimal model parameters that can be used in large-scale sea ice simulations (either global parameters, or as a function of existing model
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and strong programming skills. experience in applied deep learning and generative AI, and general competence in machine learning, including topics such as data visualisation or mathematical modelling
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with data analysis in commonly used bioinformatics programming languages, e.g. python, R, etc. Experience with image analysis in CellProfiler. Competence in machine learning or statistical modelling