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. The Department of Chemistry and Materials Science is looking for: A Doctoral Researcher (PhD student) in Machine Learning for Surface Structures The Data-driven Atomistic Simulation (DAS) group, led by
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validate the predictions of the ML models by means of atomistic modeling, in particular density functional theory (DFT) calculations, obtaining simulated electronic and emission spectra for the CDs. Finally
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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of residuals at the atomic scale and how they interact with other alloy additions, with migrating and transforming boundaries. This grant will bring together atomistic modelling (at Imperial College), atomic
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atomistic simulation methods, such as molecular dynamics, density functional theory, and machine-learning force fields, to elucidate the deformation mechanisms activated by external stimuli. The candidate
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practices. Preferred Knowledge, Skills, and Abilities: You have experience with electronic-structure or atomistic simulation workflows (e.g., VASP, Quantum ESPRESSO) and associated Python tools such as ASE
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will focus on the atomistic simulation of mineral oxide/liquid water interfaces that are of relevance to solar fuel production, decontamination of soil and geochemical transformations. The simulations
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
Temporary contract | 14 + 22 + 14 months | Belvaux Are you fascinated by data-driven atomistic simulations for materials science? So are we! Come and join us. We seek a highly motivated and capable
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using a multiscale numerical approach, from atomistic to large-scale thermomechanical modeling, coupled with micromechanical experiments (see figure below). The master internship and PhD thesis will
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extraction from atomistic systems, aiming to model the environment’s influence as colored noise. The study will involve developing and testing computational methods to characterize and incorporate memory