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the cities may occur. Extent and period: Employment under the postdoctoral agreement. Full-time for 2 years. Start date: By agreement, but preferably September 1, 2025. Content of position The working task
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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
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of electronic structure and computational materials discovery. In this role you will be performing first principles-based simulations for the description of chemical and structural dynamics in the context
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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. Strong knowledge of heterogeneous catalysis and computational chemistry. Experience with computational modeling (i,e, DFT, and/or molecular dynamics). Familiarity with software such as Material Studio
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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. Experience in conduction Life Cycle Assessments and/or Techno Economic Analysis. What we offer The postdoctoral researcher position will be filled for two years. The expected starting salary of a postdoctoral
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning