"Associate Professor of Data-Driven Material Modeling"

Associate Professor of Data-Driven Material Modeling (salary group W2)

The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Associate Professor of Data-Driven Material Modeling (salary group W2) at the Department of Materials Science. This is a full-time and permanent position to be filled by the earliest possible starting date.

We seek to appoint an internationally recognized leader in the area of data- and AI-based material modeling, focusing in particular on one or more of the following fields:

• AI-assisted modeling, optimization, and problem inversion in the area of material properties and processes
• Design and use of data spaces and digital twins for materials and autonomous material laboratories
• Use of deep learning methods to connect theory, simulation, and experiments
• Integration of high throughput experiments, simulations, and materials informatics
• Development of sustainable materials using AI-based design strategies (AI-driven materials discovery)

The professorship should strengthen and connect FAU’s research profiles in the key research priorities of new materials and processes, artificial intelligence and data science and developing future technologies. The successful candidate is explicitly expected to collaborate closely with existing structures, such as the FAU Competence Center Engineering of Advanced Materials (FAU EAM), the Erlangen National High-Performance Computing Center (NHR@FAU), the FAU Data Science Initiative, and the National Research Data Infrastructure (NFDI MatWerk, FairMat).

The professor is expected to develop an internationally visible research program linking artificial intelligence methods to modern simulation and modeling procedures, and encouraging transfer to application-oriented material systems and processes.

Furthermore, the professor will also be expected to teach in bachelor’s and master’s degree programs, in particular in the new degree program AI Material Technology. Candidates who are able and willing to teach in both English and German are preferred.

The successful candidate will have an outstanding track record in research and teaching. They are required to have a university degree and a doctoral degree in a relevant discipline and additional postdoctoral qualifications (either a habilitation or equivalent academic qualifications). These qualifications may also have been achieved in a non-university context or through a junior faculty position (for example as assistant professor).

The successful candidate is expected to carry out administrative duties and take a proactive approach to raising third-party funding. FAU pursues a policy of intense student mentoring and expects its teaching staff to be present during lecture periods.

In its pursuit of academic excellence, FAU is committed to equality of opportunity and to a proactive and inclusive approach, which supports and encourages all underrepresented groups, promotes an inclusive culture, and values diversity. FAU is a family-friendly employer and responsive to the needs of dual-career couples.

Please submit your complete application (CV, list of publications, teaching concept and research concept, list of third-party funding, copies of certificates and degrees) online at https://berufungen.fau.de by April 7, 2026, addressed to the Dean of the Faculty of Engineering. Please contact tf-dekan@fau.de with any questions.