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on developing AI-driven methods for catalyst and materials discovery, atomistic simulations, and data-driven understanding of complex energy systems. Key Responsibilities: Research Outputs: Expected to lead 1–2
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predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
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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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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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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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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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health products, e.g. toothpaste. The simulations will utilize molecular dynamics and first principles DFT codes to model enamel surfaces and their interactions with the environment of the mouth. You will
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University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC) | Spain | 2 months ago
, preferably in Python. Additional skills and experience considered positively:Experience in quantum mechanical calculations (e.g. DFT) Experience in molecular dynamics simulations Familiarity with Linux/Unix
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Computational Nanoscience group at ICN2 develops theory, models and large-scale simulation tools for quantum transport, spin dynamics and emergent computing in low-dimensional materials, in close contact with
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-learning interatomic potentials (MLIPs), density functional theory (DFT) calculations, and molecular dynamics (MD) simulations Specific Requirements PhD degree in mechanical or civil engineering, materials