Modeling and Simulation Engineer

Summary

Do you have a passion for invention and self-challenge? Do you thrive on pushing the limits of what’s considered feasible? As part of our Display Technologies group, you’ll help ensure our products’ displays work beautifully through the successful design, fabrication, and implementation of components in Apple devices. From initial concept to mass production, you’ll develop highly innovative displays for the full line of Apple products, including Apple Watch, iPhone, iPad, and Mac. Through touch and sight, these displays are how millions of Apple customers connect with our products every single day. Together, you’ll collaborate with multifunctional teams across Apple to make each interaction a magical experience to see and touch.

Description

PPO module team is looking for a highly motivated Display FEA/ Testing Engineer with deep understandings of material science, machine learning and excellent problem solving and communication skills. Develop in-house tests to collect display module failures in field conditions with thermal, mechanical, environmental and electrical stresses Develop and implement ML algorithms to assist mechanical test data collection and analysis by collecting and quantifying sophisticated physical phenomena. Work with testing and FEA engineer and to connect test output and FEA input. Propose innovative ideas and solutions to mitigate potential risks based on in-depth understanding of the simulation and testing results Take ownerships of instruments and algorithm for the organization by developing recipes, creating SOPs and training operators, in order to take the best uses of existing resources to solve problems for Apple Communicate and report analysis results to team as well as multi-functional peers and management on a regular basis.

Minimum Qualifications

PhD in mechanical engineering, material science, computer science or with +6 years’ industrial experience Deep Understanding and Hands on experience in machine learning algorithm design, modeling or quantitative analysis.

Preferred Qualifications

Hands on experience in crafting and performing material characterization. Knowledge and experience on both material characterization and machine learning are highly desired.