Computer Science | Data Science internship: failure pattern analysis

Introduction

The Leveling department within Development and Engineering at ASML develops advanced wafer measurement solutions that support nanometer-level accuracy in semiconductor manufacturing. This internship focuses on improving the understanding of machine failures through data-driven analysis of event logs. You will help transform complex and noisy machine data into meaningful insights that support system reliability and performance. Your work will contribute to identifying recurring failure patterns and enabling more effective failure classification. This internship offers the opportunity to combine artificial intelligence, machine learning, and data mining in a highly innovative engineering environment.

Computer Science | Data Science internship: failure pattern analysis

• Location: Veldhoven, Netherlands
• Team: Interns and Trainees
• Work experience: 0-1 year, See job description
• Educational background: Computer Science
• Educational background: Data Science
• Educational background: Other technical backgrounds
• Travel: No
• Workplace type: Hybrid
• Full time

Your assignment

During this internship, you will develop methods to analyze machine event logs and identify patterns related to machine failures. You will design and evaluate machine learning approaches that classify failure modes and uncover valuable insights from historical machine data. The results of your work may support engineers in understanding failure behaviour across multiple systems and improving future diagnostics.

Your main responsibilities will be:

• Develop algorithms to clean, compress, and structure noisy machine event data.
• Identify recurring failure signatures using data mining and machine learning techniques.
• Build and evaluate supervised and unsupervised classification models.
• Validate findings using historical data and expert feedback.
• Investigate similarities and differences between failure patterns across machines.
• Assess the practical value of the proposed solution for engineering use cases.
• Present results and recommendations to engineers and researchers.

This is a master's thesis/non-thesis internship for a minimum of 6 months, 5 days per week (3 days on-site).

The start date of this internship is as of February 2027 or earlier, depending on your availability.

Your profile

To be suitable for the internship, you:

• Are pursuing a master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Electrical Engineering, Systems & Control, or a related technical field.
• Have experience with Python and machine learning techniques.
• Have knowledge of data analysis, classification methods, feature engineering, and model evaluation.
• Are analytical and enjoy solving complex technical challenges using data.
• Are collaborative, proactive, and able to communicate technical findings clearly.

This internship is based in the Eindhoven region of the Netherlands. You will work closely with experienced engineers and researchers while contributing to meaningful challenges in the semiconductor industry. The assignment provides an opportunity to apply your academic knowledge to real-world engineering problems and gain hands-on experience in artificial intelligence, machine learning, and data-driven decision-making.

Other requirements you need to meet:

• You are enrolled at an educational institute for the entire duration of the internship, and you are at least 18 years old at the start of the internship;
• If you are a non-EU citizen, studying in the Netherlands, your university is willing to sign the documents relevant for doing an internship (i.e., Nuffic agreement);
• You need to be located in the Netherlands to be perform your internship. In case you are currently living/studying outside of the Netherlands, your application shows the willingness to relocate.