Researcher in computational superconductivity (m/f/x)

In order to fill a fixed-term position in full-time (39.83 hours/week = 100%) at the earliest possible date, we are looking for a

Researcher in computational superconductivity (m/f/x)

The group develops machine-learning and high-throughput ab-initio methods for the prediction, discovery and design of new materials. Our work is computational and therefore relies heavily on HPC resources.

The person should predict and characterize novel conventional superconducting compounds, in particular with interesting properties with respect to Quantum Geometry. This work will be performed with the project "SIMONS COLLABORATION ON NEW FRONTIERS IN SUPERCONDUCTIVITY", supported by the Simons foundation.

Scope: full-time

Duration: fixed-term, 1 year

Start: at the earliest possible date

Apply by: 2026-09-22

Your tasks:

- Develop and use machine learning models to predict new conventional superconductors

- Perform density-functional perturbation theory calculations of phonons and the electron-phonon interaction

- Support the experimental partners in the synthesis and characterization of the compounds of interest

- Generate phase diagrams in the experimentally interesting regions of materials space

- Investigate alloying for compounds with similar metals with machine learning and cluster expansion techniques

Your profile:

- At the time of hiring, possession of a PhD in Physics, Theoretical Chemistry, Computational Materials Science, or related fields.

- Advanced programming skills (preferably in Python).

- Experience working with the development of advanced machine learning models.

- Solid knowledge of Quantum Mechanics and Theoretical Solid State Physics.

- Knowledge of the theory of conventional and/or unconventional superconductivity.

- Willingness and enthusiasm to collaborate with other team members and experimental collaborators.

- We will prefer curious and creative researchers who have a strong motivation to help shape the direction of the project

[https://jobs.ruhr-uni-bochum.de/jobposting/3ae9b38db9641b5bad8a85624785843ba5a50c830?ref=AfA](https://jobs.ruhr-uni-bochum.de/jobposting/3ae9b38db9641b5bad8a85624785843ba5a50c830?ref=AfA)