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substrate effects using a combination of TB-SMA and Tersoff potentials. Perform atomistic simulations (molecular dynamics and Monte Carlo) to generate diverse and realistic structural configurations
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
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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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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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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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Student or Postdoc (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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molecular dynamics simulations across multiple resolutions, most likely from the atomistic to the coarse grained level, using a variety of force fields and computational methods. Run large-scale simulations
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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