Master’s Thesis in the Field of Data Science / Machine Learning in Injection Molding (f/m/d)
Develop and implement machine learning models to analyze production and process data from injection molding systems and derive actionable recommendations for process optimization. The role involves working with technical stakeholders to visualize and interpret results, with the goal of completing a master's thesis in the field of data science and machine learning.
Skills required
About this role
Working at Freudenberg: We will wow your world!
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
• 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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