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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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in modelling and simulation of nuclear materials using DFT and MD techniques. Specific Requirements Hold a Bachelor's Degree in Physics and a Master's Degree in Physics or Materials Science, or
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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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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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– Molecular-Scale Modelling This PhD position focuses on molecular-scale computational modelling of polymer-hybrid anion-exchange membranes. The project involves DFT, molecular dynamics, and Monte Carlo
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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performing DFT/MLIP molecular dynamics simulations; - Analyzing and exploiting results, as well as writing activity reports and scientific publications and presenting results at conferences and working groups
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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
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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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. The ideal candidate will have a strong background in modeling and simulation of atomic and molecular structures, along with extensive experience in analyzing the electronic and structural properties