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grand-canonical DFT, treating the electrochemical potential explicitly and the solvent implicitly. A part of the mechanistic study, related to the adsorption/desorption of reactants and products, will be
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link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and other multimodal datasets from simulation and experiment Develop benchmarking protocols and toolkits to evaluate AI models
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/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data Mine and link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and
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large-scale MD simulations, ideally with LAMMPS, demonstrated via corresponding roles in publications Experience with density functional theory (DFT) calculations, ideally with VASP, demonstrated via
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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vibrant research and development team, collaborating closely with our industry partners; Costain, National Highways, DfT and our spin-out, Didimi, and domain experts to contribute to the development of a
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DFT modelling, are a plus. Project description and responsibilities The position entails the synthesis of new ruthenium-based photocages. You will be integrated in the MCBIM group (https
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in inorganic synthesis and/or Schlenk-line preparations Experience with scientific programming (e.g., MATLAB, LabView) is advantageous Experience performing quantum chemistry computations (e.g., DFT
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including