Assistant Research Scientist; PREP

Johns Hopkins University, founded in 1876, is a leading research institution. This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP) and is funded by the CHIPS program.

$50,000 - $80,000 a year

PREP Research Associate – Research Engineer

This role supports a multidisciplinary team working to advance nondestructive defect detection metrology for advanced semiconductor packaging. The incumbent will develop reference artifacts, benchmark datasets, and automated workflows to facilitate the evaluation of defect detection and image segmentation algorithms, including those based on deep learning.

• Design 3D CAD models for simulation.
• Run X-ray computed tomography (XCT) simulations and perform XCT reconstructions.
• Develop and automate processes using Python scripts or packages, including integration with APIs or inter-process communication.
• Generate 3D models with seeded defects using generative modeling techniques.
• Analyze measurement data, perform image processing, and extract meaningful information to support research objectives.
• Organize datasets for publication and communicate results with the team.
• Present findings at internal meetings, external stakeholder meetings, conferences, and in journal publications.

• 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 and familiarity with automation of software or processes through APIs or inter-process communication.
• Experience writing Python packages or working with other programming languages such as C++ or Tcl/Tk is a plus.
• Experience with deep learning-based image segmentation is a plus.
• Experience with sample preparation (mechanical polishing, focused ion beam) or scanning electron microscopy imaging is a plus.
• Strong oral and written communication skills.
• Ability to learn quickly and adapt to new fields or techniques.
• Knowledge of software engineering for AI applications, mathematical probability, statistics, optimization methods, machine learning (supervised, unsupervised, deep learning), model evaluation, dataset bias, and AI model building is desirable.
• U.S. citizenship preferred.

The 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, veteran status, or any other legally protected characteristic.