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methods such as Density Functional Theory (DFT) and Molecular Dynamics (MD), and, optionally, in X-ray scattering or diffraction.For more information and to apply, please submit a cover letter, CV and a
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graduate students; About the project During our lifetimes, we copy approximately a lightyear’s worth of DNA, and how the different components of the molecular machinery (the replisome) work together to
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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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setting. Conduct process simulations using tools such as Aspen Plus, COMSOL, or equivalent software to predict system performance. Collaborate with engineering companies to develop a prototype carbon
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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—from cold interstellar ices to the early stages of planetary systems. The successful candidate will contribute to experimental studies simulating interstellar ice environments under controlled
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instructional faculty members and 15+ physicists in research positions. The Physics Department strives for excellence in research in Nuclear and Hadronic Physics, High Energy Physics, Atomic, Molecular & Optical
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/TranSIESTA), or Molecular Dynamics (including hybrid QM/MM or ML-IP simulations) - Apply for computational resources in HPC facilities when needed. - Prepare periodic reports of the results and provide
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researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale molecular dynamics simulations, first principles
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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