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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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, and/or numerical mathematics, as well as an excellent command of a programming language, preferably Python or C/C++. The candidate should have an interest in modeling and solving a complex, coupled
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equations (SDEs) are widely used to model time-evolving phenomena in finance, biology, and social dynamics, the underlying data is often highly sensitive. This project tackles the intersection of complex
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component, particularly magnetic components, Optimization and surrogate-modeling in Python, Integration of machine learning and numerical methods. We encourage applications from candidates with a strong
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trips, plus an elective course. Examples of elective courses could include aqueous geochemistry, geomorphology, soils, glaciology, GIS, or numerical modeling. We are seeking candidates who can demonstrate
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; Giauque et al., 2023). The PhD work is structured around the following key milestones: 1. Implementation and validation of real-gas model: Develop, verify and validate real gas equations of state capable
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numerical modelling of complex geochemical datasets to advance our understanding of the Earth system and its interconnected components of geosphere, hydrosphere, atmosphere, biosphere and associated (bio
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, testing, and analysis of resilient and sustainable geotechnical systems using centrifuge modeling. The work will bridge advanced experimental testing with complementary numerical simulation to generate
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nonlinear, ultrafast, and integrated photonics. Develop predictive theoretical and computational models for optical materials and photonic devices. Perform numerical modelling and simulation of optical
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Application of self-consistent continuum solvation models to account for solvation effects Collaborating with peers including experimental project partners Publishing results in academic and peer-reviewed