Introduction 

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. 
 

Your assignment 

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. 
 

Your profile 

To be suitable for the internship, you: 

• 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