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emerged to make meshing more flexible by allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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training network and aims to apply mathematical modelling methods to study adrenal gland steroid biosynthesis dynamics and their spatial relationship with adrenal tumours found in Primary aldosteronism (PA
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engineering, physics and applied mathematics. You should have experience in one or more of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied
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–plant interactions. Your main responsibilities will include: Developing and applying high-resolution time-lapse GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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substantially shorten preparation times and accelerate the adoption of high-order methods in industrial design and analysis. As the PhD researcher on this project, you will develop the numerical, geometric and
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multiple phase transformation, with some phases showing extensive plastic deformation, during mechanical loading and contact. The generation of particles is rooted in the complex fracture events spanning
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methods to estimate food passage do not measure food directly, are impractical for many species, and often require unnatural conditions to administer. This new method directly measures the transit and
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between