Machine-learning prediction of H trapping at grain-boundaries in ferritic alloys bei Ruhr Universität Bochum

Ruhr Universität Bochum

The Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) is looking for a

PhD student (m,f,x) for 3 years, 29.8725 hours per week, TVL E13

The Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) focuses on the development and application of a new generation of simulation tools for multi-scale materials modelling with the aim of reducing development cost and time for new materials. The different length scales from the atomic structure to macroscopic properties of materials are bridged by an interdisciplinary team of scientists from engineering, materials science, chemistry, physics and mathematics.

We are looking for a PhD student for the joint project of PD Dr. Rebecca Janisch and PD Dr. Thomas Hammerschmidt at ICAMS, Ruhr University Bochum, Germany. The successful candidate will perform atomistic simulations to predict H distribution and trapping at grain-boundaries in ferritic alloys. Hydrogen embrittlement in metals is a major challenge along the chain of H production, storage and transport. Common origin of most HE mechanisms is the high mobility of H atoms in metallic microstructures. A major requirement for optimizing metallic materials for H technology is hence the reliable prediction of H trapping and distribution in the material. In this project, we focus on understanding the solubility of H at grain boundaries (GBs) in iron and ferritic alloys. Our objective is to relate the H distribution to the local atomic environment in terms of local geometry, chemistry and stress/strain state.

extent:

part-time

duration:

temporary

beginning:

as soon as possible

application deadline:

02.01.2024

Your tasks:

The PhD student will perform high-throughput density-functional theory calculations as basis for constructing machine-learning models for the exploration of the configurational space of H atoms at grain boundaries in Fe-based alloys. The machine-learning models will be based on local structural motifs of grain-boundaries and on machine-learning interatomic potentials in the framework of the atomic cluster expansion.

Your profile:

For the PhD position, we expect a very good MSc degree in physics, chemistry, materials science or related disciplines with strong interest/background in programming, electronic-structure calculations and machine-learning techniques.

Our offerings:

Challenging and varied tasks with a high degree of personal responsibility
Team-orientated cooperation in a committed, international and appreciative team
an agile way of working
an open working atmosphere
employment at one of the largest universities in Germany within the University Alliance Ruhr

Additional information:

At the request of the applicant (m,f,x), the staff council may be involved in selection interviews. https://www.wpr.ruhr-uni-bochum.de/

If the position is funded by third-party funds the employee has no teaching obligation.

German language courses are offered by the University Language Center (ZfA) in the field of German as a Foreign Language (DaF).
https://www.zfa.ruhr-uni-bochum.de/

The Ruhr-Universität Bochum is one of Germany’s leading research universities, addressing the whole range of academic disciplines. A highly dynamic setting enables researchers and students to work across the traditional boundaries of academic subjects and faculties. To create knowledge networks within and beyond the university is RUB’s declared aim.

The Ruhr-Universität Bochum stands for diversity and equal opportunities. For this reason, we favour a working environment composed of heterogeneous teams, and seek to promote the careers of individuals who are underrepresented in our respective professional areas. The Ruhr-Universität Bochum expressly requests job applications from women. In areas in which they are underrepresented they will be given preference in the case of equivalent qualifications with male candidates. Applications from individuals with disabilities are most welcome.