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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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interdisciplinary teams. • For experimental applicants: hands-on experience with synthesis and characterization equipment. • For theory/AI applicants: experience with DFT, MD, MLIPs, or AI/ML frameworks.
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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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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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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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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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. Operate within an area of specialism (can include alloy design & modelling – CALPHAD/DFT/ICME, experimental alloy production, advanced characterisation (SEM, EBSD, TEM, STEM, APT, diffraction), and/or
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astronomical nitriles. • Performing data analysis including vibrational spectroscopy, mass spectrometry, X-ray and neutron diffraction crystallography. Perform supporting computations such as DFT calculations