AI-Driven AMHS Internship: Optimize Cross-Fab Moves

Micron Technology; Singapore; On-site; SGD 20,000 - 27,000; Full time

Benefits offered by this job

Learning opportunities

Exposure to AI-enabled workflows

Cross-functional collaboration

Job summary

Micron Technology, Inc. in Singapore invites a 6-month full-time internship to explore AI-enabled analytics for AMHS efficiency in a high-volume semiconductor fab.

You will study historical cross-fab move data, identify drivers of transfers, and evaluate AI-driven optimization opportunities to reduce non-value-added movements. You will gain exposure to semiconductor manufacturing operations, logistics optimization, data analytics, and presentations to stakeholders, with opportunities for

Qualifications

• Basic proficiency in Python, SQL, or data analytics tools.
• Familiarity with Artificial Intelligence, Generative AI, AI Assistants, or AI-enabled workflows.
• Knowledge of statistics, data visualization, or modelling techniques.
• Strong analytical and problem-solving skills.
• Effective communication and presentation abilities.

Responsibilities

• Analyze historical AMHS transportation data and movement patterns.
• Apply AI Assistants and AI-enabled analytical methods to uncover key movement drivers.
• Evaluate factors such as tool distribution, scheduling practices, staging strategies, test wafer movements, and empty FOUP transfers.
• Develop and assess potential optimization concepts using data analysis, modelling, or simulation techniques.
• Generate recommendations to improve cross-fab transport efficiency and reduce non-value-added movements.
• Present findings and improvement opportunities to project stakeholders.

Skills

Python

SQL

Data analytics

AI/Generative AI

Statistics

Data visualization

Communication

Problem solving

Education

Computer Science / Data Science / AI

Tools

Excel

Simulation tools

Job description

Micron Technology, Inc. in Singapore invites a 6-month full-time internship to explore AI-enabled analytics for AMHS efficiency in a high-volume semiconductor fab.

You will study historical cross-fab move data, identify drivers of transfers, and evaluate AI-driven optimization opportunities to reduce non-value-added movements. You will gain exposure to semiconductor manufacturing operations, logistics optimization, data analytics, and presentations to stakeholders, with opportunities for