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for 2 years You will lead and manage your own research within the project, developing and applying advanced electronic structure and molecular simulation methods. The work will involve transition-metal
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nanoparticles to uncover atomistic mechanisms for sustainable catalysis. (3) Visualizing Chemical Dynamics in Real Time You will apply advanced atomic-resolution imaging and image analysis to uncover surface
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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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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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variety of one- and two-qubit gates required for quantum computing and simulation. Si/Ge heterostructures hold various records in semiconductor spin qubit technologies [1, 2], as they provide very clean
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The Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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characterization, mechanical testing, 3D microstructural analysis, finite element simulations, atomistic modeling, and thermal transport measurement techniques to advance mechanistic understanding and predictive
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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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following skills and experience: Essential criteria Hold a PhD in Chemistry, Materials science, Physics or equivalent. Demonstrable experience in either atomistic molecular dynamics simulations or quantum
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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