Research Assistant/Associate (f/m/d) - Development of data-driven tools for the material development of metals

Institution

Lehrstuhl für Werkstofftechnik der Metalle und Institut für Eisenhüttenkunde

Our Profile

The Steel Institute and Chair of Materials Technology of Metals conducts teaching and research in the fields of steel production and processing as well as the application of metallic materials. The research is both fundamental and application-oriented; it addresses current research topics and makes a scientific contribution to industrial problems towards sustainability. Research results and the scientific staff have high national and international reputation.

Your Profile

- You have a University degree (Master or equivalent) in materials engineering, materials science or mechanical engineering with a specialisation in materials science, which you have completed with above-average success.
- You would like to work on challenging research topics within the framework of scientific research projects.
- You have a very good command of written and spoken English and German; the ability to work independently, creativity and flexibility are a matter of course for you and you are a team player.

Your Duties and Responsibilities

Conventionally, the material and process development of metals goes through several iteration loops, often based on experiments, simulations, or a combination of both. In addition, experimental planning is usually based on both, experience and trial and error.

As part of the “Internet of Production” Cluster of Excellence, approaches are being developed to implement this approach in an agile and efficient manner. Machine learning methods in particular offer promising approaches for developing the materials of tomorrow. In this context, an interdisciplinary research team has already developed data-driven tools that support experts in designing components with complex geometry for additive manufacturing, or in analyzing microstructures in an automated and reproducible manner using deep learning methods. On the materials side, what is particularly important is the further development of a suitable alloy and process design, as well as the question of how data-driven models can complement or even provide new insights into the process-microstructure-property relationships of metals?

• Use of machine learning to determine material-related relationships
• Use of deep learning for image recognition in microscopy data
• Extend existing datasets and creation of new datasets, e.g. from microscopy data
• Building interdisciplinary expertise in the fields of materials science and data science.

What We Offer

The successful candidate will be employed under a regular employment contract.
The position is to be filled at the earliest possible date and offered for a fixed term for an initial period of 2 years.
Continued employment for at least one year is planned.
The fixed-term employment is possible as it constitutes one of the fixed-term options of the Wissenschaftszeitvertragsgesetz (German Act on Fixed-term Scientific Contracts).
This is a full-time position with the possibility of a part-time contract upon request.
The successful candidate has the opportunity to pursue a doctoral degree in this position.
The salary is based on the German public service salary scale (TV-L).
The position corresponds to a pay grade of EG 13 TV-L.

About us

RWTH is a certified family-friendly University. We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports. Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket.
RWTH is an equal opportunities employer. We therefore welcome and encourage applications from all suitably qualified candidates, particularly from groups that are underrepresented at the University. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is strongly committed to encouraging women in their careers. Female applicants are given preference if they are equally suitable, competent, and professionally qualified, unless a fellow candidate is favored for a specific reason.
As RWTH is committed to equality of opportunity, we ask you not to include a photo in your application.
You can find information on the personal data we collect from applicants in accordance with Articles 13 and 14 of the European Union's General Data Protection Regulation (GDPR) at http://www.rwth-aachen.de/dsgvo-information-bewerbung.