# Internship: Machine Learning for State Model Improvement
ASML is seeking a Master student to improve the State Model using Machine Learning techniques. The State Model derives machine performance and availability information from machine-generated data aligned with the SEMI E-10 standard.
## Your Assignment
Investigate whether Machine Learning techniques can be applied to:
- Improve the quality of automatically generated machine states
- Reduce the number of corrections required during reconciliation
- Increase consistency across machines, sites, and product lines
## Key Responsibilities
- Understanding the Existing Pipeline: Event logs, task interpretation, State Model logic (states, substates, triggers), reconciliation process and EPC workflow
- Dataset Preparation: Alignment of original and reconciled states, identification of reconciliation changes, feature extraction from event logs, machine tasks and task transitions, 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
## Requirements
- Master student in Data Science or related field
- Programming experience (preferably Python)
- Strong analytical skills and structured thinking
- Proactive with ability to take ownership
- Enrolled at educational institute for entire internship duration
- Located in Netherlands or willing to relocate
- If non-EU citizen studying in Netherlands, university must be willing to sign relevant internship documents