Computer Science | Physics internship: enhance machine learning infrastructure

Introduction
The Wafer Alignment team develops the strategies that correct wafer deformation and minimize overlay errors in advanced lithography systems. Today, simulations that support alignment decisions require significant compute due to a large configuration space. Our group recently created a promising proof‑of‑concept machine learning model that reduces this load. The next challenge is to understand how such a model can be used in a real product environment. In this AI | Computer Science internship: ML integration, you help explore how machine learning can fit into a full software lifecycle at ASML.

Your assignment
In this internship you investigate how a prototype machine learning approach can be embedded into alignment software. You work with experts to understand current workflows and identify what is needed for reliable ML‑based functionality. You explore data flows, deployment considerations, and long‑term maintenance of models within a complex product environment. Your main responsibilities will be:

• Analyze how data pipelines can support continuous or periodic model training.

• Explore approaches for safe deployment, tracking, and versioning of machine learning models.

• Define testing and benchmarking methods for ML‑enabled functionality.

• Investigate how the proof‑of‑concept model can operate efficiently within the existing software architecture.

• Assess how internal infrastructures can support training, deployment, and scaling of ML models.

• Map technical gaps and propose feasible integration architectures.

• Build a prototype or concept demonstration to show how integration could work in practice.

This is a bachelors/master’s (thesis ) internship for a min 6 months but

preferably 9 months, 4–5 days per week (at least 2 days on‑site). The start date of this internship is September 2026.

Your profile
To be suitable for the internship, you:

• Are a bachelors/master’s student in artificial intelligence, computer science, data science, applied physics, or applied mathematics.

• Have strong knowledge of machine learning fundamentals and experience with common frameworks.

• Are a skilled Python user; experience with MATLAB is a plus.

• Enjoy learning how Machine Learning fits into large software systems.

• Are analytical, proactive, and comfortable working in a dynamic research setting.