Key responsibilities in your new role:As a working student, you will contribute to an audio based predictive maintenance project by focusing on pre-processing, feature extraction, and anomaly detection using a real OHT dataset. You will collaborate with our team to review and advance existing approaches as well as develop and validate new methods. Your work will create tangible value by connecting data science and signal processing with practical applications.
- Experience research: Conduct literature research on state-of-the-art audio pre-processing and feature extraction methods
- Keep up to date: Re-evaluate the work of former Infineon colleagues on the current dataset
- Data is everything: Apply a selection of new methods for audio data pre-processing and relevant pattern extraction on the OHT dataset and validate their usefulness considering anomaly detection
- Focus on the future: Compare findings with previous results, clearly highlighting the contribution and improvements from your thesis-level work
- Shaping the Future: Gain hands-on experience in data-driven audio analysis, build robust pipelines, and present insights and recommendations to stakeholders
- Take responsibility: Encourage to follow best practices in documentation, version control, and experiment traceability, fostering a culture of continuous improvement within the team
Your Profile
Qualifications and skills to help you succeed:
- Study field: Currently studying Computer Science, Electrical Engineering, Physics, Mathematic or similar
- Programming skills: Strong programming expertise in Python, Julia, or R - experience with data analysis and ML libraries is a plus
- Experience: Proficient analytical skills and ability to handle high data volume; comfortable with data preprocessing, feature engineering, and experiment design
- Skills: Fundamental skills in software development tools (e.g., GIT, testing, documentation)
- Way of working: Team-oriented, proactive, and well-organized; capable of working independently while collaborating effectively with colleagues
- Interests: Structured and reliable approach to research, experimentation, and reporting; attention to detail and commitment to reproducibility
- Language skills: Very good English skills written and spoken, German is a plus
Please attach the following documents to your application:
- CV in English
- Certificate of enrollment at university
- Latest grades transcript (not older than 6 months)
- High school report
Important information:
- Working part-time: The focus is on studies. That’s why working as a student employee during lecture period is limited to a maximum of 20 hours per week.
- Proper students (according to the German law) are welcome: To work as a student employee with us, you must be enrolled at a university and not have completed all your exams or modules for your degree program. You must not be in a semester of leave. We look forward to welcoming you to our team for at least 6 months.
- You should live close to the site: For good collaboration, it is important to us that you can come to the office regularly to integrate to the team.
