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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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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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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; and 3) high-performance computing environments. Prior programming experience is preferred but not required. The postdoctoral scientist will design, perform, and develop new
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photo-bases. The work will focus on modeling of adiabatic and nonadiabatic photochemical processes to capture excited states dynamics using an array of ab initio molecular dynamics methods for excited
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. Develop and apply ab initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings
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by time of appointment. The proposed research will leverage multiple computational many-body techniques (including classical and quantum Monte Carlo, molecular dynamics, and ab initio methods) and
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. in chemistry, physics, or related fields • Experience in performing condensed-phase electronic structure calculations and molecular dynamics simulations • Strong communication skills in English
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with all institutional training and code of conduct. Ability to work independently based on experience. Knowledge of molecular dynamics simulation and mathematical modeling. Qualifications REQUIRED
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, such as excited state methods, QM/MM simulations, molecular dynamics, simulating spectra, analyzing charges and molecular or environmental configurations, along with experience coding in python. Application