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on multiscale study of hydrogen embrittlement in steels. The primary mission of the postdoc is to support experimental efforts as well as large scale simulations by means atomistic simulations. Designing and
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characterization, mechanical testing, 3D microstructural analysis, finite element simulations, atomistic modeling, and thermal transport measurement techniques to advance mechanistic understanding and predictive
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with surface science. Experience with molecular dynamics simulations and at least basic knowledge of machine-learning approaches for atomistic modeling are highly desirable. Skills in Python and
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following skills and experience: Essential criteria Hold a PhD in Chemistry, Materials science, Physics or equivalent. Demonstrable experience in either atomistic molecular dynamics simulations or quantum
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or equivalent. 2. Demonstrable experience in either atomistic molecular dynamics simulations or quantum chemistry/density functional theory calculations 3. Demonstrable programming skills 4. Strong
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molecular dynamics simulations across multiple resolutions, most likely from the atomistic to the coarse grained level, using a variety of force fields and computational methods. Run large-scale simulations
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heat transport at the nanoscale. Couple BTE-based models with information from atomistic simulations DFT of advanced materials and thermal interfaces. Investigate phonon scattering, thermal conductivity
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel
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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic