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Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries Perform DFT to obtain atomistic insights into how tramp elements interact
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compositionally complex circular steels. As a PhD researcher, you will: Perform Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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on 2D materials and nanomaterials. Theoretical design of materials (DFT calculations). Calculation of the magnetic properties of the studied systems. Characterization of nanostructures and 2D materials
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methods such as molecular dynamics (MD) or density functional theory (DFT). • Experience with energy storage devices or materials • Knowledge of battery electrolytes, including water in salt aqueous
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methods such as molecular dynamics (MD) or density functional theory (DFT). • Experience with materials simulation tools or software is preferred. • Knowledge of battery materials, including
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will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
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learning for materials discovery or quantitative image analysis, DFT calculations for catalyst design, experiences with MATLAB / Python / AutoCAD / COMSOL • Organic synthesis, polymer chemistry, synthesis
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learning for materials discovery or quantitative image analysis, DFT calculations for catalyst design, experiences with MATLAB / Python / AutoCAD / COMSOL • Organic synthesis, polymer chemistry, synthesis