Intern - Assembly Post Electrical

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. Project Title Intelligent Equipment Health Monitoring and Reliability Analytics Using Artificial Intelligence   Project Description This project provides the intern with practical exposure to manufacturing equipment reliability, data analytics, machine learning, Artificial Intelligence, and engineering visualization.   The intern will develop a prototype that analyzes equipment alarms, downtime, maintenance, and performance data to identify equipment health trends and emerging reliability concerns. The intern will also explore AI-Enabled and natural language capabilities for communicating analytical insights to engineering stakeholders. Objective of the Project The objective is to develop an AI-Enabled equipment health analytics framework that: Monitors equipment health and reliability trends. Detects abnormal equipment behavior and emerging concerns. Identifies when performance degradation began. Highlights the main factors associated with degradation. Communicates findings through dashboards and analytical summaries   Opportunities for Full Time Employment Consideration for future internship or full-time employment opportunities will be subject to business needs, position availability, and the applicable selection process.   Project Scope The intern will be given the opportunity to: Analyze equipment alarm history, downtime records, maintenance activities, and operational performance data. Develop equipment health indicators and monitoring methodologies to evaluate reliability trends. Apply statistical and machine learning techniques to identify abnormal equipment behavior and emerging reliability concerns. Develop an Intelligent Equipment Health Monitoring Agent and interactive dashboard to communicate trends and key contributors. Document and present the methodology, findings, limitations, and recommendations to engineering stakeholders.   Learning Opportunities The intern will have the opportunity to: Gain practical experience in manufacturing data analytics and equipment reliability engineering. Apply data preparation, trend analysis, anomaly detection, and machine learning techniques. Learn to develop equipment health indicators and evaluate their effectiveness. Explore AI-Enabled Agents and natural language capabilities for engineering analytics. Strengthen technical documentation, visualization, problem-solving, and presentation skills.   Deliverables The intern is expected to deliver: A prototype of an Intelligent Equipment Health Monitoring Agent. A documented equipment health scoring methodology and set of reliability indicators. Automated models for detecting alarm spikes, downtime excursions, and equipment health degradation. An interactive dashboard showing equipment health trends, abnormal trend start points, and key contributors. A final technical report and presentation covering findings, limitations, recommendations, and future opportunities.   Impact of the Project The project is intended to: Improve visibility into equipment health and reliability trends. Enable faster identification of potential contributors to downtime and alarm excursions. Improve early detection of emerging equipment reliability concerns. Provide data-driven insights for proactive reliability improvement. Establish a scalable AI-Enabled analytics framework for future development.   Skillsets Required The ideal candidate should possess: Programming knowledge in Python, Structured Query Language, or equivalent analytical tools.