Research Assistant (f/m/d) - PhD position in multiscale fatigue damage modelling of asphalt mixtures

Applicants must hold a university degree (master's or equivalent) in civil engineering, mechanical engineering, materials science, computational engineering, applied mathematics or a related field.

A basic understanding of mechanics or material behaviour and some familiarity with programming, for example in Python or MATLAB, would be helpful. You should be interested in combining laboratory work with numerical modelling and data analysis.

An interest in one or more of the following areas is particularly welcome: asphalt or heterogeneous materials, fatigue and fracture mechanics, finite element analysis (e.g. Abaqus), DIC or CT image analysis, probability and statistics, uncertainty quantification, machine learning or optimisation. Previous experience is an advantage, but is not expected in all these areas. We particularly value motivation to learn and develop new skills.

You should bring curiosity, a responsible and proactive approach to work, and openness to feedback. You should be willing to work independently and contribute to an interdisciplinary team. Very good English skills are required. German language skills are desirable, and willingness to develop them is welcome.

Ihre Aufgaben

You will carry out doctoral research on fatigue damage identification and modelling in asphalt mixtures. You will help design and conduct cyclic indirect tensile fatigue tests, evaluate stiffness degradation and cracking, and combine DIC and CT measurements to track damage development.

You will develop and assess mesoscale finite element models representing material heterogeneity and fracture, using constitutive models and cohesive zone approaches. Experimental measurements will be used to calibrate and validate the simulations.

Building on these results, you will investigate probabilistic damage-state transitions and fatigue-life variability. You will contribute to surrogate modelling and constrained optimisation to examine how mixture composition affects fatigue performance and reliability.

You will work closely with experimental and computational researchers, document reproducible workflows, publish your findings and present them at scientific conferences. The position offers the opportunity to pursue a doctoral degree at RWTH Aachen University.

Über uns

RWTH is proud to be certified as a family-friendly university. As part of our Campus Health initiative, RWTH offers a wide range of health, counseling, and prevention services, including university sports. Employees under collective bargaining agreements and civil servants also benefit from a comprehensive continuing education program and the option to purchase a job ticket for convenient commuting.All University employees also have access to the Corporate Benefits online platform, which offers discounts on a wide range of products and services from participating companies.RWTH is an equal opportunities employer. We welcome and encourage applications from all qualified candidates and make employment decisions without regard to national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is especially committed to supporting the advancement of women in academia. When women are underrepresented in a university unit, preferential consideration will be given to female applicants among candidates of equal merit and professional standing, unless compelling reasons favor another individual.In keeping with RWTH’s commitment to equality of opportunity, we ask that you do not include a photo with your application.Please refer to https://www.rwth-aachen.de/applicant-privacy-policy for information about how we collect and process applicant data in accordance with Articles 13 and 14 of the European Union’s General Data Protection Regulation (GDPR).

Besoldung / Entgelt

EG 13 TV-L