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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image
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characterization techniques. · Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in designing and conducting in-situ TEM experiments. Familiarity with energy
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AI4Science project, specifically focusing on the intersection of advanced machine learning and sustainable catalysis discovery. The primary incentive of this Postdoctoral Fellowship is the chance to contribute
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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials
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the scientific literature relating to (and around) the project · To undertake any necessary training · To learn and develop new research skills outside own discipline · Any other reasonable duties commensurate