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areas of nanoscience and nanotechnology. Job Title: Research Assistant in Active learning Research area or group: Theoretical and Computational Nanoscience Group Description of Group/Project: In
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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 model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials. · Other research experience will be considered
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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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to support future project maintenance. Agile methodologies: Actively participate in team ceremonies (Daily Stand-ups, Sprint Planning, Retrospectives). Requirements: Bachelor's Degree in Computer Engineering
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integrating machine learning (ML) and molecular dynamics (MD) tools with experimental feedback, the project strives to accelerate the design of efficient and sustainable nanocatalysts, contributing
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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
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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characterization, and integration of machine learning to correlate synthesis conditions with functional performance. The goal is to establish predictive synthesis strategies for oxygen vacancy control, with