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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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that induce fusion between lipid vesicles and endosomes. Using mesoscopic simulations, we will identify the key structural features of proteins with a transmembrane domain that promote membrane fusion
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methods to simulate between functional chemically active surfaces and molecules/liquids. Central methodologies include: static DFT calculations; TBMD and AIMD; classical atomistic and coarse-grained
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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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biophysics/chemistry/physics and related fields Experience with Molecular Dynamics using coarse grained or atomistic models Advantage is experience with simulations of disordered proteins/polymers and
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the study of nucleation mechanisms, the analysis of out-of-equilibrium energy and thermodynamic balances, and the validation of results by comparison with experimental data and atomistic simulations
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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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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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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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) with DFT atomistic simulations to investigate the electronic structure and defect states in wide bandgap semiconductor nanomaterials. The goal is to better understand and optimize function