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at the tumour margin represent a key target for earlier and more effective therapeutic intervention. This PhD project will develop advanced MRI analysis methods and imaging-driven predictive models focusing
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mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional
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of Aeronautics, collaborating with experts in DNS/LES, dynamical systems, and data‑driven modelling. Hands‑on experience with high‑performance computing, advanced numerical methods, and ensemble‑based optimisation
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are important, leading to different models being used. However, in recent years model topologies for automatic speech recognition and many other speech processing tasks have started to converge - driven by
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target for earlier and more effective therapeutic intervention. This PhD project will develop advanced MRI analysis methods and imaging-driven predictive models focusing on the glioblastoma infiltrative
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) influence system performance and trade-offs. The research will combine analytical modelling with data-driven and AI-based methods, for example for scenario generation or uncertainty exploration. The PhD will
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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chemistry or materials science. Experience in surface science and hydrogen technology is of advantage. The ideal candidate has an independent, target- and solution-driven work attitude, inter- and
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/engineering discipline. • Good understanding of hygrothermal dynamics across different materials and scales with interest and/or experience in hygrothermal modelling. • Experience with scientific
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: • Experience of applying CFD to a complex problem • Appreciation of meshing requirements for aerodynamic simulations • Experience with machine learning or data-driven modelling techniques Funding