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, Physics, Computer Science, or a related field. Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, machine learning force field simulations). Proficiency in Python
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! You will demonstrated expertise in developing machine-learning interatomic potentials (MLIPs) for large-scale molecular dynamics (MD) simulations of materials. Together, we will push the boundaries
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their thermophysical properties. Proficiency in computational fluid dynamics (CFD) simulation of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic
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vegetables. Model bacterial pathogenic levels through complex survival and growth patterns. Utilize either data analysis or molecular analysis tools (or both) to inform strategies to optimize pre-harvest
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proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation of material properties, electronic structure, and atomic-scale behavior
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experience with multiscale modelling of materials - previous experience with molecular dynamics simulations Applications should be sent by e-mail, together with significant documents, indicating the reference
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molecular dynamics, docking (e.g., IFD), metadynamics, and free energy perturbation (FEP) techniques. Construct and contribute to the development of software tools for simulation and analysis. Integrate and
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Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT's Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning approaches
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). Brief Introduction: The project explores low-dimensional molecular magnetic systems using an integrated approach, including theory (first-principles simulations, model Hamiltonians), data-driven methods
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at the RMIT Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT’s Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning