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in AI, machine learning, and high-performance computing, uses molecular dynamics, coarse-grained simulation, and multiscale analysis to elucidate structural formation, dynamics, and the mechanisms
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inverse design. Perform high-throughput first-principles (DFT) and molecular dynamics (MD) simulations to understand mechanisms and screen candidate materials for energy and electronic applications. Build
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use of machine learning and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https
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use of machine learning and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https
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. You will implement and use particle-based simulation methods, including coarse-grained molecular dynamics, and perform rigorous verification and validation using datasets provided by our collaborators
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and society. www.tuni.fi/en The recently established BioInterfaces group at the Faculty of Engineering and Natural Sciences uses molecular dynamics simulations to tackle a diverse set of
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Engineering, Computational Science, Applied Math, or a closely related field. Demonstrated experience in atomistic and/or dislocation simulation methods (e.g., Molecular Dynamics, Dislocation Dynamics
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transition-state theory, molecular dynamics; (5) optimization theory, numerical analysis, and applied mathematics. Our Research Interests: The AI + Quantum Group focuses on the frontier of AI + Quantum
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- Beckman Institute for Advanced Science and Technology Beckman Institute for Advanced Science and Technology Job Summary Set up and perform molecular dynamics simulations of membrane transporters and other
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and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https://nyuad.nyu.edu/en/research