Data Science | AI Engineering internship: Stochastic optimization for value-driven Predictive Maintenance

Introduction

This internship is hosted within Predictive Maintenance & Diagnostics (Development & Engineering, FC‑26), closely connected to TC‑0001 Applied Data Science. Within FC-26, teams develop data‑driven methods to predict failures, optimize maintenance timing, and quantify the economic value of predictive models. A key challenge is moving beyond simple downtime metrics toward holistic cost and value evaluation, including yield impact and fleet‑level interactions.

This internship focuses on developing a multi‑modal maintenance cost model and a stochastic optimization framework to evaluate predictive maintenance strategies.

Your assignment

During this internship, you will: 

• Review academic literature on predictive maintenance, reliability, and cost‑based optimization.
  Develop a layered cost model, starting from system‑level SD/USD costs and extending to yield degradation and cluster‑level effects. Incorporate
• Incorporate interactions between multiple machines, such as ramp‑down/ramp‑up, stacking/de‑stacking, and production compensation.
• Implement stochastic simulation and optimization techniques (e.g. Monte Carlo, MCMC, probabilistic threshold search).
• Compare time‑based and model‑based maintenance strategies under uncertainty.
• Document results in a Master thesis, with potential for scientific publication.

The assignment is connected to existing ASML maintenance-simulation tooling but is explicitly framed as a research thesis, not an IT or product development task. It offers a strong combination of academic depth and real industrial relevance, with exposure to advanced topics in reliability engineering and stochastic optimization, as well as the opportunity to work on problems with direct impact on semiconductor manufacturing economics.

This is a master thesis internship for a minimum of 6 months, for 4 to 5 days per week (at least 3 days onsite). The start date of this internship is as of September 2026, but earlier is possible.
 

Your profile

To be a great match for this internship you:

• Are a master student in Data Science, Industrial Engineering, Applied Mathematics, Reliability Engineering, Systems & Control, Operations Research, or a related field.

• Have knowledge of Python (NumPy, SciPy; PyTorch/TensorFlow optional).

• Have interest or experience in Monte Carlo simulation, Bayesian inference, or optimization under uncertainty.

• Have strong analytical and structured thinking.

• Have the ability to translate problem statements into mathematical models.

• Have good communication and documentation skills.

• Are comfortable working independently in a research setting.

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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