Data Science | AI Engineering internship Stochastic optimization for value-driven Predictive Maintenance
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Job Location:
Eindhoven - Netherlands
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
Introduction
This internship is hosted within Predictive Maintenance & Diagnostics (Development & Engineering FC26) closely connected to TC0001 Applied Data Science. Within FC-26 teams develop datadriven 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 fleetlevel interactions.
This internship focuses on developing a multimodal 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 costbased optimization.
Develop a layered cost model starting from systemlevel SD/USD costs and extending to yield degradation and clusterlevel effects. Incorporate - Incorporate interactions between multiple machines such as rampdown/rampup stacking/destacking and production compensation.
- Implement stochastic simulation and optimization techniques (e.g. Monte Carlo MCMC probabilistic threshold search).
- Compare timebased and modelbased 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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Required Experience:
Intern
Key Skills
About Company
ASML gives the world's leading chipmakers the power to mass produce patterns on silicon, helping to make computer chips smaller, faster and greener.
