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research project Optimal adaptive space-time boundary and finite element methods. The project focuses on the design, numerical analysis, and implementation of novel numerical algorithms to efficiently solve
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, numerical analysis, and implementation of novel numerical algorithms to efficiently solve parabolic PDEs such as the heat equation. More precisely, you shall investigate space-time finite element methods (FEM
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models for the analysis of complex longitudinal data with repeated measurements, especially in neuroimaging and interventional neuroscience. Key topics include: linear and nonlinear mixed models with fixed
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, fluid flow, and solute transport. Develop efficient computational coupling strategies to integrate these physical processes. Apply advanced numerical methods to construct dendrite growth models, including
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Predoctoral Researcher (PhD Candidate) in Computational Simulation and AI for Therapeutic Ultrasound
of ultrasound propagation • Numerical modelling in heterogeneous media • Artificial intelligence for treatment optimisation • MRI-guided therapeutic ultrasound applications • Clinical data analysis and
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synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation
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biological sciences, social sciences, mathematical modelling, numerical computing. A strong expertise in statistical physics, modelling, numerical simulations, data analysis will be required (e.g
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duration of 6 months, governed by the Portuguese Labour Code, to perform the following duties: Development of theoretical, numerical and experimental research activities in the field of wire metamaterials
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significant impact, both on the flow and on the reflection of acoustic waves at the ground. Predicting wind turbine noise therefore relies on numerical simulations. Numerous numerical methods have been
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Experience with: Structured population models, Multiscale modelling, Numerical analysis of PDEs, Machine learning for parameter inference, Model calibration and sensitivity analysis. Familiarity with systems