> Markdown version of [/jobs/ext/3663317-graduation-assignment-data-science-for-error-patterns](https://www.wearedevelopers.com/jobs/ext/3663317-graduation-assignment-data-science-for-error-patterns). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Graduation assignment: Data Science for error patterns - **Company:** ASML - **Location:** Veldhoven, Netherlands - **Contract:** Internship / Graduate position - **Skills:** Software Code Optimization, MATLAB, Information Technology, Optimization Algorithms - **Published:** October 10, 2026 - **Apply:** https://www.adzuna.nl/details/4485254291 ## About the Role Are you a Master student with a background in Computer science or related and interested in optimization techniques and statistics? Then this graduation assignment could be interes, Are you a Master student with a background in Computer science or related and interested in optimization techniques and statistics? Then this graduation assignment could be interesting for you., The candidate must be close to complete a MSc in Computer Science, Applied mathematics or Applied Statistics. A good knowledge of optimization techniques, statistics and Matlab is a must. In addition to Matlab knowledge, a possession of a very good SW technics is required. ## Description ASML produces machines having nanometer accuracy. This means that the accuracy of the calibrations of these machines is in fraction of nanometer. The Reference and Control team within ASML is responsible for some of these sub-nanometer calibrations. In doing this one wants to find and understand error patterns: this is definitely a very complex and challenging data science problem., The primary goal of your assignment is to find hidden, and so far unknown to ASML, error patterns. These patterns must be statistically significant and therefore must be verified on big amount of data. The secondary goal of your assignment is to give a physical interpretation of the found patterns. This is a data science project and, as such requires the knowledge of data science concepts and optimization techniques like Evolutionary Algorithms, surrogate model optimization, Bayesian optimization, … You will be working with a team of experts in optimization and metrology.