Intern - F10 PHOTO PEE

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location Micron Singapore, Fab 10 (1 North Coast Drive, Singapore 757432) Department Photolithography Process and Equipment Engineering Project Title AI-Enabled Quality Drift Detection and Engineering Workflow Automation Project Description Advanced data analytics and machine learning are increasingly used in semiconductor manufacturing to improve efficiency, process control, and quality performance. This internship project focuses on applying data analytics and machine learning techniques to large-scale quality datasets for faster detection of quality-metrics drift. The intern will contribute to an end-to-end solution covering data querying, preprocessing, analytics, modelling, and results visualization. Objective of the Project Develop analytical models for monitoring key quality metrics and detecting potential drift. Apply machine learning and multivariable analysis to large-scale test and measurement data. Create automated workflows that improve engineering productivity and analytical efficiency. Develop visualizations that communicate model results and quality trends effectively.   Opportunities for Full Time Employment High-performing interns who demonstrate strong technical capability, learning agility, and successful project outcomes may be considered for future internship or full-time employment opportunities, subject to business needs and hiring requirements. Project Scope Explore machine learning techniques for feature selection, modelling, and simulation. Analyze large-scale quality, test, and measurement datasets. Perform multivariable optimization and curve turning-point detection. Develop AI-Enabled workflows and visualization tools that improve engineering productivity.   Learning Opportunities Gain hands-on experience with semiconductor manufacturing data analytics. Learn industrial applications of machine learning, predictive modelling, simulation, and optimization. Develop practical skills in data querying, preprocessing, analytics, and visualization. Collaborate with cross-functional engineering teams and subject-matter experts. Explore Artificial Intelligence and workflow-automation applications within Process and Equipment Engineering.   Deliverables Analytical models for monitoring selected quality metrics. An early quality-drift detection methodology and visualization solution. An automated workflow addressing a selected engineering-productivity opportunity. Final project documentation and presentation summarizing the methods, results, limitations, and recommendations.   Impact of the Project Improve visibility into quality-metrics performance and potential process drift. Enable earlier identification of abnormal quality trends. Enhance product and process control through data-driven insights. Improve engineering productivity through AI-Enabled workflow automation.   Skillsets Required Strong analytical thinking and general problem-solving skills. Background in data analytics, statistics, or large dataset interpretation. Programming experience in R, Python, or a similar language. Familiarity with machine learning, data visualization, or optimization concepts. Familiarity with Artificial Intelligence tools, AI Assistants, or AI-Enabled workflows is advantageous. Course of Interest The ideal candidate should be pursuing a Degree in Engineering, Statistics, Mathematics, Computer Science, Data Science, or another data analytics-related discipline. Duration of Period The ideal candidate should be able to