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Field
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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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application of Natural Language Processing Models Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job Description
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: Pr. Johan Jacquemin – johan.jacquemin@um6p.ma Research Activities Develop independent research programs in Molecular Dynamics (MD) Simulation of Materials with a focus on computational modeling and
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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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these models to interpret experimental results from neural circuit perturbations Investigate the effects of altering neuronal dynamics on brain function and dysfunction Analysis of simulated or real population
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mechanics (Monte Carlo or molecular dynamics simulation, calculation of virial coefficients from intermolecular potentials, etc.) can be used to supply data where experiments are infeasible or unavailable
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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