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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
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to provide internal operational guidance and advanced tools that accelerate the adoption and development of machine learning (ML) methods across the centre. Its mission is supported by a set of strategic lines
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+ EDX · Fully Automated FIB Helios 5UX · FEI SEM Quanta and SEM Magellan Requirements: · Education: PhD in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. · Knowledge: Deep
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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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computer-based systems and the preparation of data for inclusion in lab books, presentations and publications. Maintain a hardcopy or electronic lab book · Work in compliance with relevant Health and Safety