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experimentation, and automated laboratory systems. Keywords Materials design; Accelerated Science; Machine Learning; Autonomous Experimentation; Atomistic Simulation; Materials Genome Initiative Eligibility
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, materials science, or a related discipline, including: Background in solid-state physics and a demonstrated interest in experimental research Familiarity with scientific programming or atomistic simulations
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performs complementary atomistic simulations, you will work towards obtaining a comprehensive insights in the structure and dynamics at S-L interfaces at different states of charge. Expected results: (1
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background in developing and applying computational tools in research. Knowledge of atomistic and coarse-grained classical force fields. Experience creating and maintaining scientific software. Written and
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. References [1] Ketan S. Khare and Frederick R. Phelan Jr., "Quantitative Comparison of Atomistic Simulations with Experiment for a Cross-Linked Epoxy: A Specific Volume–Cooling Rate Analysis," Macromolecules
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years prior to the application deadline. Experience with machine learning for scientific applications. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience with atomistic
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field. Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results. Hands-on experience
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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to sustainable energy technologies. The position aims to strengthen and expand DTU Energy’s internationally recognized activities in computational materials science across electronic, atomistic, mesoscopic, and
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High