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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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of steel the microstructures will change leading to unknown product properties. The aim of this project is to advance existing micromechanical models such as the well established Crystal Plasticity method
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that electrodes strategically fouled with non-conductive plastics are better performers than perfectly clean electrodes. Electrolysis rates of organic molecules are augmented where electrode, hydrophobic insulator
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(Dr Jun Jiang) (2) In-situ formability, microstructure analysis and forming process optimization (Prof Li-Liang Wang) (3) Crystal plasticity modelling to understand how microstructural features caused
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workflows for descriptor based microstructure reconstruction to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis to establish structure–property
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tunable energy-dissipation capabilities. The proposed framework will incorporate a sliding Coulomb friction, wear resistance analysis, and a deformation mechanism, informed by insights from the crystal
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of materials mechanics, e.g., plasticity, porous plasticity, crystal plasticity and damage mechanics. Knowledge of micromechanical modelling. Knowledge of non-linear finite element methods. Knowledge of FFT
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on fatigue damage in metallic materials. We will employ 3-D crystal plasticity models in order to understand the role of compositional changes in fatigue damage. We will correlate these changes with a