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variety of one- and two-qubit gates required for quantum computing and simulation. Si/Ge heterostructures hold various records in semiconductor spin qubit technologies [1, 2], as they provide very clean
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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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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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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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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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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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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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with surface science. Experience with molecular dynamics simulations and at least basic knowledge of machine-learning approaches for atomistic modeling are highly desirable. Skills in Python and
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database