Statistical method | Internship: Bayesian methods for system-level qualification

ASML Berlin GmbH; Netherlands; Hybrid; EUR 13,000 - 18,000; Full time

Job summary

ASML Berlin GmbH invites you to join a master internship focused on data-driven decision making. You will work on developing and evaluating advanced Bayesian models to support system-level overlay qualification, applying academic methods to real engineering questions in an international environment.

You will implement and test the PoC using MATLAB, analyze complex datasets, document methodologies, and translate results into practical guidance for engineering decisions.

Qualifications

• Pursuing a master's degree in Statistics or related quantitative field.
• Experience with MATLAB and applying statistical modelling techniques.
• Knowledge of mathematical modelling and quantitative analysis methods.
• Strong analytical and problem-solving skills.
• Proactive, collaborative and able to communicate in English.

Responsibilities

• Familiarize yourself with overlay data, performance metrics and current analyses in use.
• Develop and validate a Bayesian proof-of-concept for system-level overlay qualification.
• Evaluate the approach with real and/or simulated data.
• Compare methods and translate results into engineering guidance.
• Document the statistical methodology and recommendations.
• Develop tooling in MATLAB and provide bindings if needed; ensure cross-dataset usability.

Skills

Bayesian statistics

Statistical modelling

Analytical thinking

Communication in English

Education

Master's degree in Statistics or related quantitative field

Tools

MATLAB

Job description

Introduction

Within ASML, you will join a team focused on solving complex business challenges through data-driven insights and quantitative analysis. The team supports decision-making by transforming data into actionable recommendations that improve processes and performance. During this internship, you will contribute to the development and evaluation of advanced statistical and mathematical models that help address real business questions. You will work together with experienced professionals in an innovative and international environment while applying your academic knowledge to real-world challenges. This internship offers an excellent opportunity to develop your analytical skills and create meaningful impact.

Your assignment

In this internship, you will support the team in analyzing complex datasets and developing quantitative models to improve decision-making. You will investigate statistical relationships, evaluate model performance, and translate findings into practical recommendations. The core of this assignment is to develop a proof of concept for applying Bayesian statistics to system-level overlay qualification and determine whether the approach provides sufficient practical benefit for use in our daily engineering work.

The internship provides an opportunity to apply advanced analytical techniques while working on business-relevant challenges.

Your main responsibilities will be:

• Familiarize yourself with overlay measurement data, performance metrics, machine populations, and current pre/post-change analysis practices;
• Develop and validate a proof-of-concept Bayesian statistical model for system-level overlay qualification.
• Evaluate the proposed approach using real measurement data, simulated data, or a combination of both;
• Compare the proposed methodology with current methods and translate the results into clear guidance for engineering decisions;
• Adequately document the underlying statistical methodology and provide recommendations for future use and developments;
• Implement and deliver validated tooling, which must work on multiple representative overlay datasets, to be selected during the assignment, rather than being optimized for one specific case.
• Develop the tooling in MATLAB, or provide MATLAB bindings if a different implementation is supported by sufficient technical justification

This is a master internship for 6 months, 5 days per week (3 days on-site). The start date of this internship is as of February 2027.

Your Profile

To be suitable for the internship, you:

• Are pursuing a master's degree in Statistics, Applied Mathematics, Econometrics, Data Science, or another closely related quantitative field.
• Have experience with MATLAB and applying statistical modelling techniques.
• Have knowledge of mathematical modelling and quantitative analysis methods.
• Demonstrate strong analytical and problem-solving skills.
• Are proactive, collaborative, and comfortable communicating in English.

Working at ASML means contributing to technology that enables innovation across industries worldwide. During the internship, you will gain hands‑on experience, collaborate with diverse teams, and further develop your technical and professional skills.

Other requirements you need to meet:

• You are enrolled at an educational institute for the entire duration of the internship;
• You are located in the Netherlands for the duration of your internship. If you currently live or study outside the Netherlands, your CV or motivation letter must state your willingness to relocate;
• If you are a non-EU citizen studying in the Netherlands, your university must be willing to sign the documents required for the internship (i.e., the Nuffic agreement).

This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R. § 730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.

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.

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