Research assistant in the field of mechanical engineering, materials science or comparable (m/f/d)

Cooperation in the Euratom CONNECT-NM SHIELD project: ' Long-term structural state monitoring of nuclear installations using embedded acoustic emission and machine learning to detect material wear' Development and evaluation of signal processing methods for sound emission analysis Application of machine learning and statistical methods for event detection, cluster analysis and classification Development and testing of localization methods in sound emission analysis Analysis of large experimental datasets from laboratory and field tests Participation in test campaigns in concrete structures and high-temperature systems Publication of results in international journals and conferences

Successfully completed academic studies (Master/Diplom) of the Department of Mechanical Engineering, Materials Science, Physical Engineering or Similar Promotion in a relevant field (e.g. mechanical engineering, material science, physics, signal processing, electrical engineering) is advantageous Very good knowledge of signal processing and data analysis Knowledge of machine learning processes Experience in sound amisison analysis, ultrasound methods or analog ZfP/SHM methods Proven experience in acoustic emission analysis or closely related NDT/SHM methods (e.g. ultrasound, guided waves) Knowledge of the spread of elastic waves in solid bodies (e.g. volume waves, guided waves) Experiences in one or more of the following areas: use of fiber-optic measuring systems, simulation of the excavation of elastic waves, application of machine learning in signal analysis, construction and use of measuring technology Programming experience (e.g., Python, MATLAB) Very good English skills in word and writing Ability to work independently and take responsibility for tasks – the role requires a high degree of self-organisation and initiative German knowledge is advantageous Good communication and information behaviour, targeted and structured working methods, initiative/readiness for use and capability, willingness to make decisions and skills, team capacity and willingness to cooperate, willingness to learn, flexibility and conceptual, strategic and innovative thinking skills

Attractive and modern work environment with excellent infrastructure and equipment at the latest scientific level (Labore, BAM Data Store, High-Performance Computing etc...). Attractive location Possibility of mobile work up to 60% 30 days for a 5-day week Flexible working time A responsible, interesting and varied activity in a competent and collegial environment. Work in national and international networks with universities, research institutions and industrial companies