AI-Driven NAND Reliability Data Analytics Intern

Micron; Singapore; On-site; SGD 13,000 - 27,000; Full time

Job summary

Micron Technology, Singapore invites an AI-Enabled NAND Reliability Data Analytics and Technology Learning Intern to join the TD - NAND Reliability team for a 6-month internship. You will work under mentors, perform data-driven analysis on NAND reliability, develop visualizations, and explore AI-enabled techniques to improve engineering learning and efficiency.

Candidates pursuing a PhD in related fields are preferred.

Qualifications

• Strong analytical, critical-thinking, and problem-solving abilities.
• Effective written and verbal communication skills.
• Familiarity with data analysis, statistics, and data visualization techniques.

Responsibilities

• Analyze reliability datasets to investigate sources of variation.
• Develop visualizations and technical summaries to communicate findings.
• Apply statistical and data-driven methodologies to identify trends and learning opportunities.
• Explore AI-Enabled analytical techniques to improve data interpretation and insight generation.
• Develop or enhance analysis workflows, scripts, or tools that improve reproducibility.
• Utilize Python and data analysis tools to streamline engineering learning cycles.
• Collaborate with mentors and project stakeholders.

Skills

Analytical skills

Critical thinking

Problem solving

Written communication

Verbal communication

Data visualization

Python programming

AI interest

Team collaboration

Education

PhD in Electrical/Electronics/Materials/Applied Physics/Semiconductor Engineering

Tools

Python

Jupyter notebooks

Data analysis tools

Job description

Micron Technology, Singapore invites an AI-Enabled NAND Reliability Data Analytics and Technology Learning Intern to join the TD - NAND Reliability team for a 6-month internship. You will work under mentors, perform data-driven analysis on NAND reliability, develop visualizations, and explore AI-enabled techniques to improve engineering learning and efficiency.

Candidates pursuing a PhD in related fields are preferred.