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approach must be combined with mechanistic models that describe the specific microstructure elements. A variety of inputs from both experimental work and simulations (i.e., first principle, atomistic, and/or
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), uncertainty quantification, and atomistic simulations within the FNR-funded UMLFF project. MLFFs have transformed atomistic simulations, offering quantum-chemical accuracy for large systems. However, they
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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Offer Description Development of atomistic ab-initio simulations and machine learning models for the study of phonon transport, phase transitions, and structural optimization of phase change materials
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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on multiscale study of hydrogen embrittlement in steels. The primary mission of the postdoc is to support experimental efforts as well as large scale simulations by means atomistic simulations. Designing and
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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applications for a PhD Student (f/m/d) in the field of Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules Possible projects are variational free energy methods
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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