Doctoral research assistant in the field of civil engineering, physical engineering, physics, mathematics or cf. (m/f/d)

The Federal Institute for Materials Research and Testing (BAM) is a federal scientific and technical authority based in Berlin. As a departmental research institution of the Federal Ministry for Economic Affairs and Energy, we examine and advise on the protection of people, the environment and property. Our activities in materials science, materials technology and chemistry focus on the technical safety of products and processes. Become part of our team of dedicated employees! Neural operators are combined in this project with classic numerical methods to enable efficient approximation and analysis of parameterized finite element models. The combination of data-based and FEM-based methods creates a hybrid model that accurately reflects physical relationships and at the same time enables real-time predictions for digital twins. To strengthen our team in the field of 7.7 'Modeling and Simulation'' in Berlin-Steglitz on 01.07.2026

PhD student*r scientific assistant (m/w/d) of the Department of Civil Engineering, Physical Engineering, Physics, Mathematics or cf. Group E13 TVöD Time contract for 2 years Full time/parttime

Development and implementation of thermo-mechanical models in FEniCSx Integration of FEM models in Neural Operators and PINNs Development and implementation of methods for quality assessment of metamodels Use of meta models for model calibration with uncertainties Documentation of methods Compilation of scientific publications and participation in the development of research applications Presentation of results at scientific meetings

Compulsory: Graduate / Master degree and PhD in Civil Engineering, Physical Engineering, Physics, Mathematics or a comparable degree course Proven knowledge and practical experience in the development of FEM applications (e.g. with FEniCSx) In-depth knowledge in the field of scientific programming, preferably in Python, including experience with the implementation of ML methods (e.g. PyTorch) Very good English knowledge in word and writing as well as good presentation and publication skills It would also be desirable: Experience in machine learning / deep learning (e.g. PINN and neural operator procedures such as DeepONet, FNO) You should also bring the following: Initiative / commitment and ability Team skills and cooperation Target-oriented and structured operation Willingness to make decisions and abilities Good communication and information behaviour

A varied and demanding activity within a top-class network of politics, science, business and society Work in national and international networks with universities, research institutions and industrial companies Attractive and modern work environment with excellent infrastructure and equipment at the latest scientific level (Labore, etc.) A responsible, interesting and varied activity in a competent and collegial environment Good compatibility of work and private life [Work-Life-Balance] (possibility of mobile work [up to 60 %], flexible working time design, 30 days leave and use of up to 12 days of equalisation per year and part-time opportunities) Open welcome culture, a certified family-friendly working environment, regular feedback talks and competent contact persons, sustainability [including a grant to the job ticket]