Bachelor thesis, project thesis, master thesis, working student Student Assistant in Sensors, Signal Processing & Machine Learning

Are you interested in gaining hands-on experience in sensor technology, signal processing, and machine learning while contributing to an innovative research project? Join the SMASH research project and support the development of a cutting-edge system for real-time monitoring of forging tool wear. You will work with advanced acoustic emission analysis (AEA), intelligent signal processing methods, and machine learning techniques to develop solutions for data-driven tool condition monitoring.

Your tasks

• Research on project-related topics
• Setup, installation, and calibration of vibration and acoustic emission sensors on the test tool
• Processing and analysis of measurement data using digital signal processing methods
• Support in the development, training, and validation of machine learning models

Your profile

• You are studying mechanical engineering, mechatronics, electrical engineering, computer science, data science, or a comparable technical degree program.
• You are motivated, work independently, and have an interest in sensors, measurement technology, signal processing, and data-driven methods.
• Ideally, you already have initial knowledge or practical experience in working with sensors, particularly in the field of vibration or acoustic emission measurements, as well as digital signal processing.

We offer

• Competitive compensation
• Independent and self-responsible work
• Flexible working hours that are easy to balance with your studies
• Well-equipped workplaces
• Home office by arrangement
• Experimental testing and execution
• Possibility of long-term collaboration