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Veldhoven, Noord-Brabant, Nederland
Hybrid
The EUV Optical Modeling and AI team at ASML develops advanced methods to understand and improve the performance of EUV optical systems. In this Internship, you will work at the intersection of physics, machine learning, and optical diagnostics. You will use unique measurement datasets, physics-based simulation models, and advanced computing resources to uncover hidden information from complex optical measurements. Your work will contribute to a deeper understanding of EUV system behavior and support future diagnostic innovations. This Internship offers the opportunity to combine scientific research with practical impact in a highly advanced technology environment.
As part of this Internship, you will apply computational physics and modern AI methods to improve the interpretation of diagnostic measurements and infer physical properties that cannot be measured directly. Depending on the selected research topic, you may work on inverse modeling, representation learning, accelerated simulation methods, or multimodal diagnostics. You will develop and validate models using both simulated and experimental data while balancing performance, robustness, uncertainty awareness, and physical realism. Your main responsibilities will be:
Develop AI and computational physics models for EUV optical diagnostics
Analyze large and complex datasets to identify meaningful physical patterns and behaviors
Build methods to infer hidden physical parameters from diagnostic measurements
Design, train, and benchmark machine learning models against baseline approaches
Validate results using simulated and experimental data while assessing robustness and uncertainty
Develop clean, reusable, and well-documented Python workflows and research tools
Present research findings, recommendations, and outcomes to the team
This is an internship for minimum 6 months, minimum 5 days per week. During the first month, you are expected to work on-site 5 days per week to support onboarding, collaboration, and knowledge transfer. After the first month, there may be the possibility to work 1 to 2 days per week from home, subject to the project requirements and agreement with your supervisor. The preferred start date is as soon as possible, but candidates who are available to start up to February 2027 are also encouraged to apply.
Are pursuing a bachelor's or master's degree in computational physics, physics, artificial intelligence, data science, computer science, applied mathematics, optics, or a related technical field
Have strong Python programming skills and experience with scientific computing, scientific data analysis, or machine learning
Have a solid foundation in physics, mathematics, and machine learning fundamentals
Are analytical and capable of working with large and complex datasets
Are proactive, curious, and comfortable exploring unfamiliar physical phenomena while seeking feedback and collaborating with others
Experience with PyTorch, numerical simulation, physics-based modeling, inverse problems, uncertainty quantification, Bayesian methods, optimization techniques, or high-performance computing is considered beneficial.
At ASML, we believe diverse perspectives drive innovation. During this Internship, you will collaborate with researchers and engineers from different disciplines while developing expertise in both AI and physics. The project combines advanced machine learning techniques with a strong understanding of physical systems, making it ideal for students who enjoy applying data-driven methods to solve scientific challenges. By the end of this Internship, you will have delivered a tangible research outcome, such as a validated model, research prototype, benchmarking study, reusable software solution, or new diagnostic insight. If you are excited by the combination of artificial intelligence, physics, and real-world technological impact, this Internship could be the right opportunity for you.
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.
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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