Assistant Research Scientist; PREP

PREP Research Associate – CHIPS Funded Project

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST partners with academic institutions to jointly conduct scientific research, and staff who receive a PREP award participate in collaborative projects. The role focuses on advancing nondestructive defect detection metrology for advanced semiconductor packaging.

Salary: $50,000–$80,000 per year

• Design and develop 3‑D CAD models for simulation.
• Run X‑ray computed tomography (XCT) simulations and perform XCT reconstructions.
• Prepare samples for focused‑ion beam/SEM and nanofabrication.
• Develop Python scripts/packages to automate processing workflows.
• Utilize team‑developed generative modeling to create 3‑D defect‑seeded models.
• Evaluate defect detection and image segmentation algorithms, including deep‑learning approaches.
• Analyze measurement data, conduct image processing, and extract insights to support research objectives.
• Organize, document, and prepare datasets for publication.
• Present findings at internal meetings as well as external stakeholder gatherings.
• Publish results in peer‑reviewed journals and present at conferences.

• Master’s degree in physics, engineering, or a related discipline.
• Experience with XCT measurements, reconstruction, and image analysis.
• Experience with XCT simulation is a plus.
• Proficiency in Python scripting; familiarity with automation via APIs or inter‑process communication is a plus.
• Experience writing Python packages or using other programming languages (C++, Tcl/Tk) is a plus.
• Experience implementing deep‑learning‑based image segmentation processes is a plus.
• Experience with sample preparation (mechanical polishing, focused‑ion beam) or SEM imaging is a plus.
• Strong oral and written communication skills.
• Rapid learner who can adapt to new fields or techniques.
• Knowledge of: software engineering for AI applications; mathematical probability, statistics, and optimization methods; machine learning (supervised, unsupervised, deep learning); dataset bias and labeling issues; AI model building; translating operational needs into solvable AI problems.
• U.S. citizenship preferred.

Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. The university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. Qualified individuals have access to all programs, benefits, and activities on the basis of performance and merit, without regard to irrelevant personal factors.