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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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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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-state hydrogen storage and compression technologies. A key aspect of the role will be the computational discovery, design, and modelling of metal (complex) hydrides using state-of-the-art atomistic and
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experience in atomistic simulations is preferred. • Development or working experience of generative models in scientific applications (e.g., diffusion/flow model) and working experience with agents
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-state hydrogen storage and compression technologies. A key aspect of the role will be the computational discovery, design, and modelling of metal (complex) hydrides using state-of-the-art atomistic and
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for candidates with backgrounds in areas such as: Computational materials design, including atomistic and electronic structure approaches AI for materials science, including interatomic potentials and generative
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synchrotron x-ray scattering experiments on complex materials using Rietveld and atomic pair distribution function techniques. Ability to test and refine atomistic structure models against experimental x-ray