Student Assistant (f/m/non-binary) – Machine Learning in Geomechanics

At the Chair of Geotechnical Engineering in Civil Engineering and the Institute for Geomechanics and Underground Technology, led by Univ.-Prof. Dr. Raul Fuentes, we work on the entire spectrum of geotechnical and tunnel construction applications in teaching, research, and scientific expert opinions.

You can get an overview of our current activities on our website https://www.gut.rwth-aachen.de

You have/are:

• Enrolled in an engineering program (civil, mechanical, computational engineering, or similar)
• Fluent in English
• Basic knowledge of programming (python, matlab)
• Familiar with or strong interest in neural networks and machine learning concepts

We’re seeking a motivated Student Assistant to contribute to two key areas: the development of a new course titled "Physics-Informed Neural Networks Applied to Geotechnical Engineering" and support for ongoing research.

• Actively participate in creating course materials, including coding examples, exercises, and potentially aiding in instructional content.
• Provide support to students by addressing their inquiries and explaining course concepts.
• Assist with ongoing projects, mainly focused on using neural networks to simulate complex problems in soil mechanics and soil dynamics.

The successful candidate will be employed as a student assistant.
The position is to be filled at the earliest possible date and offered for a fixed term of 1 year.
This is a part-time contract position.
The standard weekly hours will be 8 hours.
The salary is based on the RWTH Guidelines for Student and Graduate Assistants.
The position corresponds to a pay grade of 13.25 € per hour.

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.