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multiphysics models to investigate aquifer-based compressed air energy storage (CAES) systems. The research will involve coupling fluid flow, heat transfer, geomechanics, and potentially reactive transport
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and extend existing numerical codes to simulate these phenomena. Some experiments and modelling will be done in collaboration with other PhD students in the GRAIL project. Your tasks: • Simulate
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] (AI-assisted Simulations of Microstructure driven MEchanical properties from high Throughput and multiscale analysIS), in the framework of PEPR DIADEM[2] , which aims to develop an advanced
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-agent systems. Methodology: Integrate ToM models with Reinforcement Learning based frameworks for single-agent and multi-agent decision-making. Develop simulation environments capturing realistic human
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heterogeneity. Simulations and sensitivity analysis of the model will be used to identify optimum dosing schedules or ADC properties that maximise the anti-tumour response. This project is in collaboration with
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approach (CPlantBox) to mechanistically simulate plant growth, plant-soil interactions and the rhizosphere microbiome. You will contribute to model development and apply it to disentangle the role of root
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avionics triggered by the interaction of the energetic particles with the electronics. The project will adapt and extend existing numerical codes to simulate these phenomena. Experiments and modelling will
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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement
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modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex
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the construction of 3D models of protein complexes inserted in membranes to quantuim chemistry calculations, including classical molecular dynamics simulations. The major points that will be under scrutiny during