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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