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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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of chemically storing and releasing hydrogen, using methanol as a reservoir. Main activities: • Utilize global optimization codes and perform DFT calculations on supercomputers. • Analyze results and
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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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Description: Apply Position Description We invite talented and motivated applicants for PhD and Postdoc positions in the group of Haowei Xu (https://www.haoweixu.com/ ) at the Department of Physics, City
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experience with DFT codes will be very highly valued. • Knowledge of chemical reactions and how to model them through computer simulations is highly valued. • Knowledge of classical molecular dynamics
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into polymer matrices. - Use of luminescent species for applications such as sensors. - Knowledge of DFT‑type simulation methods for modeling molecular properties. - Experience writing scientific articles and
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to compute the predicted ZTs via first principles calculations. - Computer simulations: ML + DFT - Scripting (Python) - Analysis of the results + writing publications - The position is part of an ANR-DFG
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simulations and large-scale DFT calculations at finite temperature using the Special Displacement Method (SDM) and its anharmonic extension (ASDM). Model polymorphous structures beyond the standard monomorphous
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Healthcare Monitoring We welcome applications from those with expertise in or across these disciplines: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale
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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