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the Department of Materials Science and Engineering at National University of Singapore under the direction of Dr. Jing Yan and Dr. Peichen Zhong. This role involves using computational methods, including DFT
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computational simulations in the area of density functional theory (DFT). The role will focus on understanding and predicting the properties of novel functional materials, including surfaces and interfaces
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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/materials science, AI/ML, and computational modelling (DFT, MD, etc.) Good publication record Good written and verbal communication skills Proficiency in programming We regret that only shortlisted candidates
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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a Research Fellow to contribute to a project focused on focused on data-driven discovery of atomic catalysts. Key Responsibilities: Theoretical predictions using DFT and machine learning, and
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with demonstrable experience in chemical purification and characterization of starting materials and reaction products by standard techniques (NMR, IR, Mass spectrometry, X-ray diffraction, TEM, and DFT
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density functional theory (DFT) methods integrated with data-driven artificial intelligence techniques. Research may encompass the development and application of machine learning force fields, high
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Computational Chemistry, Catalysis, Energy Chemistry, or a related field. Extensive expertise in Density Functional Theory (DFT) for material design and optimization. Proficiency in computational tools and