3 Jun 2026

Data Science Internship: Improving Machine State Classification with Machine Learning

ASML

Veldhoven

3 Jun 2026

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Data Science internship at ASML to improve machine state classification with machine learning. The intern will investigate ML techniques to enhance the automatic State Model, reduce manual reconciliation, and increase consistency across machines, sites, and product lines. The assignment includes understanding the existing pipeline, dataset preparation, ML model development, evaluation, and recommendations.

AIHybridFull-timeInternshipPythonMachine Learning

Data Science Internship: Improving Machine State Classification with Machine Learning

Apply

AIHybridFull-timeInternshipPython

Salary

Not specified

Work Location

Veldhoven, North Brabant, Netherlands, NL

Work Model

Hybrid: minimum 2 days on-site, minimum 4 days per week

Employment Type

Full-time

Experience Level

Internship

Contract Length

6 months

Core Qualifications

Technical (Must-have)

PythonMachine LearningData ScienceEvent logsFeature extractionSupervised learning

Soft Skills

analytical skillsstructured thinkingproactiveownership

Key Responsibilities

•Understanding the Existing Pipeline (event logs, task interpretation, State Model logic, reconciliation process and EPC workflow)
•Dataset Preparation (alignment of original states and reconciled states, identification of reconciliation changes, feature extraction from event logs, machine tasks and task transition, and selected information from logbooks)
•Machine Learning Model Development (supervised learning using reconciled states as ground truth, prediction of machine states and/or state transitions, justification of model choice and features)
•Evaluation (comparison of Current State Model output, ML-based predictions and reconciled states, quantitative assessment of potential quality improvements)
•Conclusions and Recommendations (feasibility of ML support for state interpretation, possible integration approaches, limitations, risks, and explainability considerations)

InternshipData ScienceMachine LearningASMLVeldhovenSemiconductorState ModelPythonHybrid

Key Responsibilities

•Understanding the Existing Pipeline (event logs, task interpretation, State Model logic, reconciliation process and EPC workflow)

•Dataset Preparation (alignment of original states and reconciled states, identification of reconciliation changes, feature extraction from event logs, machine tasks and task transition, and selected information from logbooks)

•Machine Learning Model Development (supervised learning using reconciled states as ground truth, prediction of machine states and/or state transitions, justification of model choice and features)

•Evaluation (comparison of Current State Model output, ML-based predictions and reconciled states, quantitative assessment of potential quality improvements)

•Conclusions and Recommendations (feasibility of ML support for state interpretation, possible integration approaches, limitations, risks, and explainability considerations)