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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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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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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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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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heat transport at the nanoscale. Couple BTE-based models with information from atomistic simulations DFT of advanced materials and thermal interfaces. Investigate phonon scattering, thermal conductivity
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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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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis
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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