Intern - Product Yield Analysis and Analytics (Research)

Design new methods to detect fails during semiconductor manufacturing Develop and optimize tools for defect detection used by multiple engineering teams Find opportunities to use ESDA data for yield and reliability improvements Apply machine learning and machine vision techniques to improve defect analysis Currently pursuing a PhD or equivalent experience in Data Science or a related field with expected graduation in or after 2026 Strong skills in machine learning and machine vision Knowledge of electrical engineering and semiconductor fundamentals Proficiency in at least one programming language Excellent problem-solving and analytical skills Strong communication skills for presenting complex technical concepts Experience in software development for engineering applications Familiarity with defect analysis tools and semiconductor manufacturing processes Ability to work independently and collaboratively in a technical environment Interest in applying AI to manufacturing and yield improvement challenges