PhD/Postdoc Position in Acoustic Emission Monitoring, Signal Processing, and Machine Learning at BAM Bundesanstalt für Materialforschung und -prüfung in Germany

Bundesanstalt für Materialforschung und -prüfung (BAM) is advertising a 3-year fully funded research position suitable for a PhD or postdoc in an interdisciplinary project within SHIELD, part of the Euratom CONNECT-NM initiative.

The work is hosted at BAM’s Division 8.2 (Non-Destructive Testing Methods for Civil Engineering) in Berlin and focuses on acoustic emission (AE) monitoring of nuclear facilities. The research combines signal processing, machine learning, event detection, denoising, clustering, and localization on real experimental data from laboratory and field campaigns. It sits at the interface of materials science, mechanical engineering, non-destructive testing (NDT), structural health monitoring (SHM), and nuclear energy.

The project involves collaboration with major research and industrial partners, including CEA, Alma Mater Studiorum – Università di Bologna, KU Leuven, EDF, and Westinghouse Electric Company. The BAM job description also mentions testing campaigns on concrete structures and high-temperature systems, publication of results in journals and conferences, and work with large experimental datasets.

Eligibility highlights include a master’s or diploma degree in mechanical engineering, materials science, physical engineering, or a comparable field; a PhD is an advantage for the role. Strong signal processing and data analysis skills, knowledge of machine learning, experience with AE or related NDT/SHM methods, solid understanding of elastic wave propagation, and programming experience in Python or MATLAB are expected. Excellent English is required; German is an advantage.

The position is funded through mid-2029 and the BAM posting lists a temporary contract for 36 months, salary group 13 TVöD, and full-time or part-time options. The application deadline is 2026-07-28.

Apply through the online application form referenced in the job ad, or submit by post using Job-ID J000000151. Review the full posting before applying to confirm documents and submission details.