In the research project "Application of ML methods to plain bearing systems for wear prediction," methods for domain adaptation for plain bearings are to be researched and practically implemented within the framework of an FVA project in cooperation with RWTH Aachen University (Institute for Machine Elements and Systems Engineering). In addition to developing the methodology, extensive measurements are to be carried out on an existing test rig.

• Successfully completed university degree (Master, Diplom, or equivalent) in Electrical Engineering, Computer Engineering, Mechanical Engineering, Automotive Systems, Mechatronics, Physical Engineering, or Computational Engineering Science
• The ability to report and prepare documents in German at FVA Steering Committee meetings is required; good English language skills are also required
• Good to very good knowledge of: Technical diagnostics, measurement technology, basic mechanical engineering knowledge, preferably bearing dynamics and wear, machine learning, statistics and forecasting methods, signal processing, modeling and simulation, programming in Python, C, and MATLAB

Please send your complete application with the reference number and the usual documents (combined in a single pdf file, max. 5 MB) by email to ewa.heinze@tu-berlin.de.

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To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.