Doctoral student in mathematics, technomathematics, scientific computing, physics or an engineering science with a proven strong focus on numerical methods (m/f/d)

PhD in the framework of a DFG joint project to evaluate the reliability of non-destructive testing methods and procedures for structural monitoring (Non-Destructive Evaluation / Structural Health Monitoring) The aim is to develop and validate a hybrid approach from simulation and experiment to analyze the reliability (probability of detection) of non-destructive testing and monitoring methods using ultrasound. The core of the doctoral project is the development of simulation methods – primarily based on the Scaled Boundary Finite Element Method (SBFEM) – to model the propagation of guided ultrasonic waves in pipelines The task includes the further development of existing SBFEM implementations and their extension to coupled field problems (multiphysics simulation) Working in an interdisciplinary research team with external and internal partners

Successfully completed academic studies (Master or Diploma) of the Department of Mathematics, Technomathematics, Scientific Computing, Physics or Engineering with a proven strong focus on numerical methods Proven knowledge in the development of numerical methods for modelling wave propagation and structural dynamics (e.g. FEM, semi-analytical methods) Experiences in one or more of the following areas are desirable: wave propagation in solid bodies, guided ultrasonic waves, acoustics, non-destructive testing Knowledge of SBFEM or related processes is advantageous sound programming knowledge in at least one scientific programming language (e.g. Python, Matlab, C++) is required; Experience with version control (Git) is desired Willingness to support students (Bachelor and Master's thesis) Embossed initiative and joy in developing innovative solutions at the interface of numerics and experimental work Good communication and information behaviour, targeted and structured working methods, initiative / willingness to use and skills, team capacity, willingness to cooperate and willingness to learn

30 days for a 5-day week Attractive and modern work environment with excellent infrastructure and equipment at the latest scientific level (Labore, BAM Data Store, High-Performance Computing etc.). Work in national and international networks with universities, research institutions and industrial companies An extensive and multi-disciplinary training programme Fair payment by collective agreement We offer: Professional and overtime support by the BAM Graduate Academy, including competence building and networking formats. Access to high-quality training offers and training on digitalization topics such as programming, research data management, AI applications and machine computing in science. Networking and exchange in interdisciplinary digitization communities as well as BAM digitalization events with the aim of developing innovative solutions with experts. .