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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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to develop and implement continuum formulations of stress-based poro-damage dynamics and hydrofracturing, using new e.g. phase field methods. Models will be validate against date obtained from inverse
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new biological phenomenon, the formation of biomolecular condensates by liquid-liquid phase separation (LLPS), and how this emerging field can lead to entirely novel concepts to treat brain diseases
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phases of matter with unprecedented resolution and sensitivity. We aim to achieve this goal through the confinement of charges and photons in reduced dimensions. We welcome candidates with strong