Thesis: Multi-Class Classification for Quality Control in Low Pressure Coating Processes

IonKraft; Aachen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A sustainable technology company is offering a thesis opportunity in quality control to enhance recyclable plastic packaging. The role involves applying machine learning techniques to improve process monitoring and quality standards. Candidates should be enrolled in a relevant degree and possess strong analytical and programming skills, particularly in Python. Bilingual proficiency in German and English is required. This role provides personal and professional growth in a pioneering field of sustainability.

Leistungen

Personal and professional growth

Opportunity to make an impact in sustainability

Pioneering and exciting work

Qualifikationen

• Strong background in machine learning, particularly supervised learning.
• User and programming skills in Python.

Aufgaben

• Specify relevant classes for the application on a PECVD reactor.
• Acquire spectral data on the prototype machine.
• Implement a program using scikit-learn.
• Create a robust module by applying scikit-learn.
• Interpret hypotheses, methods, and results independently and critically.

Kenntnisse

Machine learning

Python programming

Analytical skills

Problem-solving

Bilingual (German and English)

Ausbildung

Enrolled in relevant degree program (mechanical engineering, computer science, CES, chemistry, or physics)

Jobbeschreibung

The Position

We are a spin-off of RWTH Aachen University, which develops a special coating technology to enable recyclable plastic packaging for various applications. We are offering a thesis in the field of quality control to help build a knowledge base that will significantly advance the circular economy for plastics in Germany and the world.

Plastic packaging plays an essential role in our daily lives by storing and protecting products and extending their shelf life. It is crucial to demonstrate that our coating meets the quality standards required for packaging that comes into contact with food, cosmetics, or highly reactive substances. Currently, this is assessed using binary classification. To better monitor the process and identify the causes of insufficiently coated packaging, we aim to research the application of multi-class classification strategies. Your thesis will have a real impact on our work. The scope can be adapted for a bachelor's or master's thesis.

Your Mission

• You specify relevant classes for the application on a PECVD reactor

• You acquire spectral data on our prototype machine

• You implement a program using the data with scikit-learn

• You create a robust module by applying scikit-learn

• You interpret your hypotheses, methods, and results independently and critically

Your Profile

• You are enrolled in a relevant degree program, e.g. mechanical engineering, computer science, CES, chemistry, or physics

• You have a strong background in machine learning, particularly supervised learning

• You have user and programming skills in Phyton

• You are characterized by analytical and problem-solving skills and you are proactive and independent

• You are business fluent in German and English

What we offer

• Growth: We help you grow personally and professionally and give you much personal responsibility

• Impact: Your work makes a real difference in the packaging industry towards more sustainability

• Variety: Your thesis is pioneering and exciting and offers many opportunities to expand your skill set