Intern, Reliability and Inspection - Summer 2027

Corning•City of Corning, NY

2d•$42,510 - $82,810

About The Position

The Summer 2027 intern will support the development and validation of laboratory methods for detecting and characterizing small defects in brittle materials and multi-material structures. The work will examine damage that forms during thermal-mechanical reliability stresses as well as defects identified through post-stress inspection, establishing each method’s detection capabilities and how detections relate to underlying failure mechanisms. With guidance from technical staff, the intern will set up and calibrate sensing and inspection systems, conduct reliability experiments, analyze waveform and image data, and verify detections against independent ground truth from microscopy, fractography, sectioning, materials and property characterization, and failure analysis. Depending on interests and program needs, the project may focus on acoustic emission or ultrasonic monitoring during thermal cycling, commissioning and applying an automated imaging and machine vision inspection system, or a combination of both. Results will also support calibration of physics-based models and empirical reliability predictions. The internship is for 10 weeks during May–August 2027. Applicants must have a graduation date of December 2027 or later, be willing to work at the posted job location, and be interested in working for Corning after graduation if offered a full-time opportunity. The position does not support immigration sponsorship.

Requirements

Currently pursuing a graduate degree in mechanical, materials, reliability, electrical or computer engineering, applied physics, optics, or a closely related field; a PhD is preferred.
Research, coursework, or hands-on laboratory experience in at least one of acoustic emission, ultrasonics or non-destructive evaluation; optical imaging or machine vision; or reliability testing, life modeling or failure analysis.
Experience with signal or image processing using MATLAB, Python, or similar tools.
Ability to follow laboratory safety procedures, experimental protocols, and data-documentation practices.
Strong analytical and problem-solving skills, attention to detail, and willingness to learn new hands-on laboratory techniques.
Ability to organize, analyze, and communicate experimental results clearly in written and verbal formats.
Ability to work collaboratively with scientists and engineers while independently completing assigned tasks.
Availability for 10 weeks during Summer 2027 (May–August 2027) and a graduation date of December 2027 or later.
Interest in working for Corning, Inc. after graduation if presented with a full-time job opportunity.
Willingness to work at the posted job location.

Nice To Haves

PhD research in non-destructive evaluation, acoustics/ultrasonics, or imaging applied to reliability of electronics and semiconductor packaging.
Experience with acoustic emission sensors, waveguides, source location, waveform feature extraction, or laser vibrometry.
Experience setting up cameras, optics, motion stages, or automated inspection systems, including machine-learning-based image analysis.
Experience with reliability testing of electronics or semiconductor packages, such as thermal cycling or environmental stress testing.
Knowledge of brittle materials, glass, fracture mechanics, or thermal-mechanical modeling.
Experience with microscopy, cross-sectioning, sample preparation, or other materials characterization methods.
Familiarity with experimental design, statistical data analysis, and technical literature review.

Responsibilities

Review literature on non-destructive evaluation, acoustic emission and ultrasonic sensing, optical inspection, and reliability mechanisms in brittle materials and electronic packaging.
Plan and conduct experiments, including building and calibrating sensors, data acquisition systems, and test or inspection setups.
Apply acoustic emission or other ultrasonic methods to detect, locate, and time damage during thermal cycling and other reliability-related stressors.
Commission and apply a laboratory imaging system, including automated image analysis and machine vision for defect detection.
Acquire, process, and analyze sensor waveforms and image data using MATLAB, Python, or similar data-analysis tools.
Correlate detected events with independent ground truth from microscopy, sectioning, materials characterization, and failure analysis; quantify detection thresholds, noise, and false calls; and identify which damage mechanisms can and cannot be detected.
Use collected data to help calibrate physics-based modeling predictions and develop empirical reliability predictions and associated models.
Document methods so others can repeat them, follow laboratory safety practices, and communicate findings clearly in written reports and presentations.