Postdoctoral Researcher (m,f,x) in atomistic modelling and machine learning for multicomponent alloy

Ruhr-Universität Bochum

44801 Bochum - Deutschland

Wissenschaftliche/r Mitarbeiter/in Arbeit Vollzeit ab 01.03.2026

Beschreibung

Postdoctoral Researcher (m,f,x) in atomistic modelling and machine learning for multicomponent alloys, TVL E13, for 3 years

The Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) is a research centre at the Ruhr-Universität Bochum, focusing on the development and application of multi-scale simulation tools for advanced materials. ICAMS is embedded into a network of national and international collaborations. Researchers and students benefit from this top international and interdisciplinary environment. The Department for Atomistic Modelling and Simulation has its focus on machine-learning interatomic potentials and employs these for predicting materials properties. The advertised position will be based in the Atomistic Simulation of Compositionally Complex Alloys Group, which studies the mechanical, thermodynamic, and magnetic properties of advanced multicomponent alloys.

The position involves performing density-functional theory and atomistic simulations to investigate the phase stability of multicomponent alloys. The work includes the development and application of machine-learning interatomic potentials combined with molecular dynamics and Monte Carlo simulations. A central objective is the inclusion of thermal and magnetic excitations in predictive thermodynamic modeling of compositionally complex alloys.

Scope: full-time
Duration: fixed-term,
Start: by March 01, 2026 (alternative starting date possible upon mutual agreement)
Apply by: 2025-12-29

Your tasks:

- Perform DFT and MLIP simulations for magnetic multicomponent alloys.
- Perform molecular dynamics and Monte Carlo simulations.
- Developing models to compute phase stability, including magnetic and lattice excitations.
- Implement methods in Python workflows.
- Publish and present results in international journals and conferences.

Your profile:

- PhD in physics, materials science, computational engineering, or chemistry, with a strong background in atomistic or computational materials modeling.
- Experience in density functional theory calculations and machine-learning interatomic potentials.
- Experience with LAMMPS, molecular dynamics, and Monte Carlo simulations, as well as with the simulation of magnetic systems and magnetic models.
- Programming experience in Python.

[https://jobs.ruhr-uni-bochum.de/jobposting/eee4bb36fc8e840b400d31a1842a4bbe0ab8185d0?ref=AfA](https://jobs.ruhr-uni-bochum.de/jobposting/eee4bb36fc8e840b400d31a1842a4bbe0ab8185d0?ref=AfA)

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