Data Science | Computer Science internship: proactive support analytics in Veldhoven, Nederland

Introduction The DUV Global Support Center (GSC) provides advanced support for ASML’s DUV platforms from locations in the Netherlands, Taiwan, and the United States. Within the Netherlands-based team in Veldhoven, this internship focuses on helping identify potential machine performance issues before they impact operations. By analyzing large datasets and developing predictive models, you will contribute to a more proactive support approach. This internship offers a unique opportunity to work with technical experts, gain insight into a global support environment, and make a measurable impact. If you enjoy solving complex data challenges, this internship could be a great fit for you.

Your assignment

During this Data Science | Computer Science internship: proactive support analytics, you will work closely with experienced engineers and data specialists to identify early warning signals in machine data. Your analysis and predictive models will help detect trends that indicate when systems may require proactive investigation. Depending on project priorities, you may also contribute to the improvement of diagnostic analysis capabilities. Your main responsibilities will be:

• Analyze large datasets to identify trends, patterns, and early warning signals.
• Develop and improve predictive models using Python.
• Create data-driven insights that support proactive decision-making.
• Validate model performance and recommend improvements.
• Collaborate with technical specialists to understand machine behavior.
• Present findings and recommendations to stakeholders.
• Contribute to additional data analysis or diagnostic improvement projects when applicable.

This is a bachelor/master internship for minimum 7 months, minimum 4 days per week (2 days on-site). The start date of this internship is early February 2027.

Your profile

To be suitable for the internship, you:

• Are pursuing a master's degree, or are in the final phase of a bachelor's degree, in data science, computer science, informatics, software engineering, or a related field.
• Have experience with Python and SQL.
• Are analytical and enjoy working with large datasets.
• Are proactive, curious, and eager to learn in a technical environment.
• Work effectively with colleagues from different backgrounds and communicate clearly in English.

This internship is a non-thesis internship based in Veldhoven. A hybrid working arrangement is available, combining on-site collaboration with remote work. We encourage students who are motivated by data, technology, and continuous learning to apply for this internship. Please submit both your CV and a cover letter. In your cover letter, we would like to learn more about your motivation for this internship. 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 attach your cover letter with a clear motivation on why you are interested in this internship assignment in particular;
• 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.

Inclusion and diversity

ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that inclusion and diversity is a driving force in the success of our company. Do you want to know more about the internship allowance, visa support and applying for an internship at ASML? Read this [page](#).