Student Assistant (f/m/non-binary) – In the field of Life Cycle Assessment

at  RWTH Aachen

YOUR PROFILE

We are looking for a motivated student assistant to support the SBQC-PO research project for the development of a sensor-based inline quality monitoring system for the processing of shredded lightweight packaging waste. The project aims to develop an AI-based material flow characterisation and automated quality management system. The project not only includes the development of AI models, but is also actively involved in setting up suitable experimental tests, preparing materials and analysing experimental data. You have:

• Knowledge or high interest in the field of machine learning, computer vision and sensor data
• Programming experience in Python
• Knowledge of image and sensor data processing or time series analysis is a plus
• Analytical thinking and an eye for detail
• High motivation and good communication skills
• Ability to work independently and willingness to work in a team
• Good command of the English language
• Current enrollment at a university or university of applied sciences

ABOUT US

RWTH is a certified family-friendly University. We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports. Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket.
RWTH is an equal opportunities employer. We therefore welcome and encourage applications from all suitably qualified candidates, particularly from groups that are underrepresented at the University. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is strongly committed to encouraging women in their careers. Female applicants are given preference if they are equally suitable, competent, and professionally qualified, unless a fellow candidate is favored for a specific reason.
As RWTH is committed to equality of opportunity, we ask you not to include a photo in your application.
You can find information on the personal data we collect from applicants in accordance with Articles 13 and 14 of the European Union’s General Data Protection Regulation (GDPR) at http://www.rwth-aachen.de/dsgvo-information-bewerbung.
Application
Number:
V000008568
Application deadline:
18/10/2024
Mailing Address:
RWTH Aachen University
Chair of Anthropogenic Material Cycles and Institute of Processing, Coking, and Briquetting
Aydin Deliktas
Wüllnerstraße 2
52062 Aachen
Email:
Applicants are invited to submit their applications via email. For data protection reasons, however, we recommend sending applications via mail

• Collaboration in the research project ‘SBQC-PO’: You will support us in the development of a system for the assessment of plastic flows using machine learning methods
• Analysing and processing hyperspectral data using Python
• Support our scientific staff by independently researching literature, preparing and analysing experimental data and creating scientific illustrations
• Supporting the institute with general tasks