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experience with DFT codes will be very highly valued. • Knowledge of chemical reactions and how to model them through computer simulations is highly valued. • Knowledge of classical molecular dynamics
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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of radionuclides on clay mineral surfaces using DFT Kinetic Monte Carlo simulations with activation energy barriers as input to simulate large-scale interactions of nuclides with surfaces Preparation and
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implement a software engine that automates fault model generation, evaluation, and management. Design and implement advanced test generation methodologies (e.g., test algorithms, Design-for-Test (DfT), Memory
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking for a Postdoctoral Fellow to contribute to building computational
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methods and workflows for chemical problems and experience using simulation software Demonstrated experience with various computational chemistry techniques: DFT-, force-field- and/or molecular-dynamics
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by metal nanoparticles, cell survival and radioresistance. The MS-RADAM research programme combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory
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and graduate levels (minimum grade of 16 out of 20). Good oral and written English communication skills are required. Experience in computational modeling (DFT and ab initio) with benchmarking
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nanoparticles, cell survival and radioresistance. The MS-RADAM research programme combines state-of-the-artc omputational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics
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complex materials simulations. These agents will assist with setting up, executing, and optimizing electronic structure workflows, from standard ground-state Density Functional Theory (DFT) calculations