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Field
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energy piles have been extensively studied, using field tests, physical modelling, and numerical modelling, the behaviour of energy piles under complex thermomechanical loading (e.g. vertical-horizontal
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, NTNU, will host the PhD position. The topic of the PhD fellowship is at the interface of numerical mathematics, generative AI models, and computer science. A successful candidate will be offered a three
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supports numerical computational modeling of subsurface flow and well performance, mentors PhD students, and contributes to ongoing research projects. This is a temporary, part-time (wage-payroll) hourly
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the Netherlands and the United States and will employ 11 PhD candidates, 1 EngD candidate and 4 postdoctoral researchers. You will closely collaborate with another PhD candidate developing optimization and
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novel turbulence closures that explicitly account for the mutual interactions between turbulence and sediment dynamics. Using a combination of analytical techniques, asymptotic methods, and numerical
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University is home to numerous world-class research groups and institutes, and maintains strong connections with leading research organizations. CML is part of the Faculty of Science at Leiden University
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description Internal erosion and piping are among the main failure mechanisms of clay–sand layered dikes and remain important challenges in flood risk management. Reliable numerical models can improve our
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risk management. Reliable numerical models can improve our understanding of how piping develops and help engineers assess and design safer, more reliable flood defence systems. In this PhD project, you
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or more of the following topics: numerical methods for (partial) differential equations. optimization or inverse problems. data assimilation or uncertainty quantification. agentic and generative AI. solid
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-time operation. The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power