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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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Australian National University | Canberra, Australian Capital Territory | Australia | about 10 hours ago
research ideas. Experience in conducting molecular dynamics simulations—including free energy calculations or advanced sampling techniques—is essential, ideally involving proteins, nucleic acids, or other
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-throughput measurements of molecular binding with simulations and quantitative modeling to gain a physical understanding of life at the molecular level. We are recruiting a Postdoctor that wants to use and
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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computational simulations and related fields Theoretical knowledge of Molecular Dynamics and free energy calculations Some years of work experience in simulations with proteins/membranes and their analysis
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profile: Must-have: PhD in computational simulations and related fields Theoretical knowledge of Molecular Dynamics and free energy calculations Some years of work experience in simulations with proteins
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). Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, phase-field simulations). Proficiency in Python, TensorFlow/PyTorch, and scientific computing. Experience in
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& design, catalyst design & modelling, and carbon capturing & conversion. Proficiency in fluidized reactor design & optimization, hydrodynamics & molecular dynamics modelling (openFOAM, Ansys Fluent, LAMMPS
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an interdisciplinary and international team of experts in antibiotic drug discovery, molecular simulations, structure-based drug design, covalent enzyme inhibition, organic synthesis, and medicinal chemistry. We will
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for molecular dynamics simulations. 2. Familiarity with thermal conductivity and viscosity calculations. 3. Experience with machine learning methods. 4. Familiarity with data analysis and