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Postdoc f/m/div in Computational Materials Desig ... (No replies)
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We welcome applications by excellent researchers for a
Postdoc f/m/div in Computational Materials Design: Application of new machine learning potentials including magnetism
This position is limited to 3 years.
Topic
The project aims to advance finite-temperature ab initio approaches based on recently developed machine-trained potentials including all relevant excitation mechanisms related to vibrations, configurational entropy, magnetism, and their mutual coupling effects. The project is part of a collaborative research effort with the groups of Prof. Grabowski (University Stuttgart) and Prof. Shapeev (Skoltech). In particular a novel type of machine learning potentials including the magnetic degree of freedom (developed in the group of Prof. Shapeev) will be applied and validated to technologically relevant material systems.
Your profile
PhD degree in theoretical or computational physics, chemistry, material science or corresponding fields. The ideal candidate should have prior experience in at least one of the following areas: application of DFT for magnetic systems, LAMMPS simulations, developing and applying of magnetic model Hamiltonians (e.g. Monte Carlo simulations of Heisenberg-like systems) / large-scale magnetic simulations, molecular dynamics simulations.
Preferred starting date is 1.1.2021 (negotiable).
Applications via the online interface.
For more information about the position, please contact Fritz Körmann directly.