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Master’s Thesis in the Field of Data Science / Machine Learning in Injection Molding (f/m/d)

Freudenberg Technology Innovation SE & Co. KG
Weinheim

Jetzt bewerben

Details zur Stelle

vor 13 Tagen

Qualifikationen

TensorFlow
Doktorat
Informatik
Englisch
Master
Machine Learning
Produktion
Python
Deutsch
Data Science

Vollständige Stellenbeschreibung

Working at Freudenberg: "We will wow your world!" This is our promise. As a global technology group, we not only make the world cleaner, healthier and more comfortable, but also offer our 52,000 employees a networked and diverse environment where everyone can thrive individually. Be surprised and experience your own wow moments. Together with our partners, customers and the world of science, we develop leading-edge technologies, and excellent products and services for about 40 markets and for thousands of applications: seals, vibration control components, batteries and fuel cells, technical textiles, filters, cleaning technologies and products, specialty chemicals and medical products.

Some of your Benefits

Cafeteria/ Canteen

Cafeteria/ Canteen: We offer fresh food on-site, both hot and cold.

Diversity & Inclusion

Diversity & Inclusion: We focus on providing an inclusive environment and recognize our diversity contributes to our success.

Easily Reachable

Easily Reachable: Easy, low-stress access by car or public transport.

Public Transportation Allowance

Public Transportation Allowance: Commute more affordable thanks to public transport allowance.

Employee discounts

Employee discounts: Opportunities for deals on products and services.

Weinheim

On-Site

Freudenberg Technology Innovation SE & Co. KG

The increasing digitalization of production processes opens up new opportunities for data-driven optimization of manufacturing operations. In injection molding in particular, extensive process and machine data offer significant potential for the application of data science and machine learning methods.
The objective of this master’s thesis is to develop and evaluate data-driven models for the analysis, prediction, and optimization of injection molding processes.

You support our team as

Master’s Thesis in the Field of Data Science / Machine Learning in Injection Molding (f/m/d)

Responsibilities

You will analyze and prepare production and process data from injection molding systems
You will identify relevant influencing factors affecting part quality, cycle time, and scrap rates
You will develop and implement machine learning models (e.g., regression, classification, anomaly detection)
You will evaluate the models in terms of predictive performance and industrial applicability
You will visualize and interpret the results for technical stakeholders
You will derive actionable recommendations for process optimization
You will document your results and prepare the scientific thesis

Qualifications

You are currently enrolled in a degree program in Data Science, Computer Science, Mechanical Engineering, Mechatronics, Industrial Engineering, or a comparable field.
You have strong knowledge of statistics, data science, and machine learning
You have experience with Python and common libraries (e.g., pandas, scikit-learn, PyTorch/TensorFlow)
You have an interest in production processes, ideally with knowledge of injection molding or manufacturing technologies
You have very good German or English language skills, both written and spoken
You ideally have an interest in pursuing an industrial PhD following your thesis

The Freudenberg Group is an equal opportunity employer that is committed to diversity and inclusion. Employment opportunities are available to all applicants and associates without regard to race, color, religion, creed, gender (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity or expression, national origin, ancestry, age, mental or physical disability, genetic information, marital status, familial status, sexual orientation, protected military or veteran status, or any other characteristic protected by applicable law.

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Easily Reachable

Public Transportation Allowance

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