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Computing. · Experience with High Throughput Calculations will be valued but it is not essential. · Previous knowledge of Density Functional Theory (DFT) and experience with DFT codes will be very highly
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candidate would work on projects in methods development for coupled electron-nuclear dynamics based on the exact factorization approach and time-dependent density functional theory. Projects involve theory
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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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advanced characterization methods of inorganic materials and their assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics
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Physics, Materials Science, Chemistry, Chemical Engineering, Applied Physics, or a closely related field with a focus on computational materials modeling. Density Functional Theory (DFT) for surfaces and
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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. Quantitative theory for linear dynamics of linear entangled polymers. Macromolecules, 35:6332, 2002. [3] M. Müller. Memory in the relaxation of a polymer density modulation. J. Chem. Phys., 156:124902, 2022. [4
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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thin films possessing the desired magnitude and direction of the polarization. The successful candidate will perform atomistic simulations, using both density functional theory and classical molecular
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, chemistry, computational science, or a related field. Strong expertise in at least two of the following: density functional theory (DFT)/many-body methods, molecular dynamics (MD), machine learning (ML