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Field
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. For emerging low-dimensional (D) materials controlling the function at an atomistic scale is the key for their application in optoelectronics. Among them, 2.5 D materials (a combination of (twisted) layered
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computational approaches — from coarse-grained elastic network models (ANM/GNM) to atomistic and enhanced-sampling molecular dynamics and machine learning/AI methods — to understand how biomolecules achieve
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-supervised by Prof. Jenny Nelson (Imperial College London), and Dr. Riccardo Rurali (ICMAB-CSIC). Main tasks: • Develop atomistic and coarse-grained models of the structure and dynamics of molecular
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for candidates with backgrounds in areas such as: Computational materials design, including atomistic and electronic structure approaches AI for materials science, including interatomic potentials and generative
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searching for a computational postdoctoral research associate. The project is associated with atomistic plasma-surface interaction simulations, employing machine learned interatomic potentials (MLIPs
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device-level properties of amorphous graphene nanoribbons and related structures. This will provide a bridge between atomistic material modelling and experimentally measurable device characteristics
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 12 hours ago
located in Pittsburgh, Pennsylvania and focuses on advancing oil and gas technologies that strengthen U.S. energy security and resiliency. This project aims to provide atomistic understanding of mechanical
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Do you want to use advanced simulation methods to understand and improve the performance of fuel cells? Join Chalmers and contribute to atomistic modelling of catalytic reactions to enhance
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on various aspects along the battery value chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoc project Atomistic modelling and synthesis
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, interactomics, structural biology, and imaging datasets into predictive computational frameworks. Application of atomistic simulations, coarse-grained modeling, RNA folding prediction, RNA-protein and RNA-small