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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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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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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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processes, multimodal data fusion, physics–ML hybrid modelling (from CFD to atomistic simulations), and AI-assisted hypothesis formulation. MSCA Doctoral Candidate eligibility criteria Applicants must comply
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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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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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that induce fusion between lipid vesicles and endosomes. Using mesoscopic simulations, we will identify the key structural features of proteins with a transmembrane domain that promote membrane fusion
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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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biophysics/chemistry/physics and related fields Experience with Molecular Dynamics using coarse grained or atomistic models Advantage is experience with simulations of disordered proteins/polymers and