Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathematics, or a related field

Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathematics, or a related field

To strengthen our team in the division 7.7 “Modeling and Simulation” in Berlin-Steglitz, starting 01.07.2026, we are looking for a

Salary group E13 TVöD
Temporary contract until for 24 month
Full-time / suitable as part-time employment

The Bundesanstalt für Materialforschung und -prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry. We perform research and testing in materials science, materials engineering and chemistry to improve the safety of products and processes. At BAM we do research that matters. Our work covers a broad array of topics in the focus areas of energy, infrastructure, environment, materials, and chemistry and process engineering. We are looking for talented people to join us.

In this project, neural operators are combined with classical numerical methods to enable the efficient approximation and analysis of parameterized finite‑element models. By integrating data‑driven and FEM‑based approaches, a hybrid model is developed that accurately represents physical relationships while providing real‑time predictions for digital‑twin applications.

Your responsibilities include:

• Development and implementation of thermo‑mechanical models in FEniCSx
• Integration of FEM models into neural operators and PINNs
• Development and implementation of methods for assessing the quality of the metamodels
• Use of the metamodels for model calibration under uncertainty
• Documentation of the developed methods
• Preparation of scientific publications and contribution to research proposals
• Presentation of results at scientific conferences

Your qualifications:

Mandatory requirements:

• A completed university degree (Diploma/Master’s) and a Ph.D. in civil engineering, engineering physics, physics, mathematics, or a comparable field of study
• Demonstrated expertise and practical experience in the development of FEM applications (e.g., using FEniCSx)
• Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch)
• Excellent spoken and written English, as well as strong presentation and publication skills

Desirable requirements:

• Experience in machine learning / deep learning (e.g., PINNs and neural‑operator methods such as DeepONet, FNO)

In addition you have:

• A high degree of initiative and commitment
• Ability to work in a team and willingness to cooperate
• A goal-oriented and structured approach to work
• Willingness and ability to make decisions
• Good communication and information management skills

We offer:

• A varied and challenging job within a high-caliber network from politics, science, business and society
• Work in national and international networks with universities, research institutions and industrial companies
• Attractive and modern working environment with excellent infrastructure and state-of-the-art scientific equipment (laboratories, etc.)
• A responsible, interesting and varied job in a competent and collegial environment
• Good work-life balance (possibility of mobile working [up to 60%], flexible working hours, 30 days of vacation and up to 12 days of compensatory time off per year as well as part-time options)
• An open welcoming culture, a certified family-friendly working environment, regular feedback meetings and competent contact persons, sustainability [including a subsidy for a job ticket]

Your application:
We welcome applications via the online application form. Alternatively, you can also send your application by post, quoting the reference number 49/26 - 7.7 to:
Bundesanstalt für Materialforschung und -prüfung
Referat Z.3 - Personal
Unter den Eichen 87
12205 Berlin
GERMANY
www.bam.de
Dr. Unger will be glad to answer any specific questions you may have. Please get in touch via the telephone number +49 30 8104-3787 and/or by e-mail to joerg.unger@bam.de.