Master's Thesis in the Field of Data Science / Machine Learning in Injection Molding
Role details
Job location
Tech stack
Job description
- 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
Requirements
Do you have a Master's degree?, * 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
Benefits & conditions
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.
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