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Field
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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-throughput first-principles (DFT/MD) simulations, and generative AI to predict, interpret, and design materials for energy storage, energy conversion, and electronic applications. The successful candidate will
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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Theory (DFT) Microstructure prediction and evolution Secondary expertise in the following topics is desired but not required: Molecular dynamics Materials informatics Machine learning and artificial
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information, first-principles calculations (DFT), or many-body numerical methods are particularly encouraged to apply.
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Chern numbers; nonlinear Hall effects - 2D materials in energy storage contexts (e.g., batteries, supercapacitors) • Conduct state-of-the-art computational work to complement theory, including: - DFT
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. An ideal candidate should have experience in modeling electrochemical reactions on surfaces and interfaces using first-principles density functional theory (DFT), grand canonical DFT (GC-DFT
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catalysis. Strong programming skills in Python; experience with scientific computing and data analysis. Experience with first-principles calculations (e.g., DFT), molecular dynamics, or machine learning
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behaviour and electrically tunable interfaces in 2D heterostructures. Methods include density functional theory (DFT), atomistic simulation, high-performance computing, and machine-learning-assisted materials
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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