Assistant Research Scientist; PREP

PREP Research Associate – CHIPS Funded Project

Position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP), a 5‑year cooperative agreement between NIST laboratories and participating U.S. universities. This role focuses on advancing nondestructive defect detection metrology for advanced semiconductor packaging.

The candidate will join a multidisciplinary team of scientists and support the research by:

• Designing CAD models and running X‑ray computed tomography (XCT) simulations.
• Conducting XCT reconstructions to generate benchmark datasets.
• Preparing samples for focused ion beam/scanning electron microscopy (FIB/SEM) and nanofabrication.
• Developing Python scripts or packages to automate processes and integrate a generative‑modeling workflow for producing 3D models with seeded defects.
• Analyzing measurements, performing image processing, and extracting information to support experiment goals.
• Organizing datasets for publication, presenting results at internal meetings, external stakeholder meetings, conferences, and journal submissions.

• Design 3‑D models for simulation, run XCT simulations, carry out XCT reconstruction, and execute image analysis.
• Organize and prepare data sets for publication.
• Present results at internal meetings and occasional meetings with external stakeholders.
• Publish results in 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 (plus).
• Proficiency in writing Python scripts, automating or controlling software via APIs or inter‑process communication.
• Experience with Python packages, C++, or Tcl/Tk (plus).
• Experience with deep learning–based image segmentation (plus).
• Experience with sample preparation (mechanical polishing, focused ion beam) or SEM imaging (plus).
• Strong oral and written communication skills.
• Ability to learn rapidly and adapt to new techniques.
• Knowledge of software engineering for AI applications, statistical methods, machine learning, and translating operational needs into AI problems (plus).

U.S. Citizenship preferred.

Johns Hopkins University is an equal opportunity employer and 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 any other legally protected characteristic.