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learning potentials can overcome typical bottlenecks of empirical potentials for simulating dislocations, cracks and hydrogen diffusion near precipitates embedded in ferritic iron. While considerable
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and quantifying the HE mechanisms, since hydrogen is challenging to detect experimentally. Machine learning potentials can overcome typical bottlenecks of empirical potentials for simulating
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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Offer Description Deploy detailed models of coupled ocean / sea ice processes, probe their turbulent interactions, compare with observations, and design parameterizations for global climate models. Job
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engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and
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researcher. The Veldhorst group is a pioneer in quantum computing and simulation in germanium. They have demonstrated the first single-hole qubit, enabled two qubit logic in germanium, executed quantum
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mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room
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largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing applications. As AI models continue to increase in size and computational complexity
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of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after
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simulation and multiscale modelling methodology to obtain novel and improved understanding of the behaviour of (bio)materials, and of complex (bio)chemical and physical processes that are of technological and