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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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to be enrolled in a study programme leading to a PhD (or in a non-degree programme). - Have proven experience in physical and digital modeling to predict the structural performance of packaging, enabling
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for our environment? Will the streets ever by quiet again? This PhD will give you the opportunity to build models that predict this! Job description In 2023, global drone shipments reached 1 million
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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are considered the largest source of uncertainty in climate predictions because it is complicated to accurately model the small-scale process (microphysics) inside clouds occurring in a range from meters to
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning
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transfer This research combines advanced numerical simulation and artificial intelligence to develop predictive models for high-temperature multiphase flows, with specific relevance to steel casting
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of the fluids under consideration. This will be coupled with the use of in-house models that can be employed to explore and predict the behaviour of newly developed fluids in different components and applications
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the fields of fairway design, intelligent waterway engineering and autonomous shipping, handling of new fuels on ships and in ports, and AI-based predictive analytics for ensuring maritime safety. Enhancing