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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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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
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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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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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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molecular dynamics simulations, applicable to materials science, biomolecules, or a related field. Programming experience (e.g., Python), with a strong background in developing and applying computational