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Field
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that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate ultrafast chemical dynamics at surfaces and in materials. Your personal sphere of influence
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to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate
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operations and chemical processes. Ability to conduct experiments. Experience in using both steady-state and dynamic modeling, simulation and optimization tools, e.g. Aspen, Pro/II, GAMS, gProms... Ambitious
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with programming (e.g. Python, Julia), simulation methods (e.g. molecular and quantum dynamics) and tensor network calculus. What we offer: In the Maurer group, we aim to develop computational simulation
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
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. Demonstrated experience in performing simulations at the atomic-scale, such as density functional theory (e.g. VASP, Ab-init, Quantum espresso), molecular dynamics (e.g. LAMMPS, DL-POLY), development of inter
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experts in mRNA production, molecular dynamic simulations, and in vivo studies. Key Responsibilities: To carry out mammalian cell culture and live cells assays related the delivery of nucleic acids inside
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are welcome. Skills are not essential but experiences in the following areas are preferred: biological physics, quantitative biology, nonlinear dynamics, stochastic simulations, dynamic systems, numeric
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School of Engineering Sciences at KTH Job description The research project concerns the development and use of MINFLUX single-molecule microscopy for cellular imaging and dynamic studies
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collaboration with the national (GTK, CSC) and international project partners (Karlsruhe Institute of Technology). Perform ab initio molecular dynamics (MD) simulations together with consortium partners