Postdoctoral Fellow; PREP

CHIPS Funded Project

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires that such institutions be the recipients of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas.

Employees in this position will perform technical work that underpins the scientific research of the collaboration.

The role involves technical support for the collaboration, contributing to the development and execution of research activities related to nondestructive defect detection metrology for advanced semiconductor packaging.

Research Engineer (CHIPS Project: Nondestructive defect detection metrology for advanced semiconductor packaging)

The candidate will join a multidisciplinary team of scientists working to advance nondestructive defect detection metrology for advanced semiconductor packaging by developing reference artifacts and benchmark datasets. The candidate will contribute to designing CAD models, running X-ray computed tomography (XCT) simulations, performing XCT reconstructions to generate datasets, and developing a Python script or package to automate these processes. Additionally, the candidate will utilize a generative modeling process created by the team to help generate 3D models with seeded defects.

The datasets will be used to evaluate defect detection and image segmentation algorithms, including those based on deep learning principles. The incumbent will analyze the resulting measurements, perform image processing, extract meaningful information to support the research goals outlined in the experiment plan, organize the measured and analyzed datasets for publication, communicate with the team, and share the results at conferences and in publications.

• Design 3D 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 results at conferences.

• A doctoral degree in physics, engineering, or a related discipline.
• Experience with XCT measurements, reconstruction, and image analysis; experience with XCT simulation is a plus.
• Experience in writing Python scripts; familiarity with automating or controlling other software, tools, or processes through APIs or inter-process communication is a plus.
• Experience writing Python packages or using other programming languages such as C++ or Tcl/Tk is a plus.
• Experience implementing deep learning-based image segmentation processes is a plus.
• Strong oral and written communication skills.
• Able to quickly learn and adapt to new fields or techniques.

• CV/Resume (limit to 3 pages; include a valid email address; exclude personal details such as self-portraits, phone number, home address, citizenship status, languages spoken, sex/gender).
• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender

Authority: 15 U.S.C. 278g-1(e)(1) and (e)(3) and 15 U.S.C. 272(b) and (c).

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered may vary and will ultimately depend on multiple factors, including geographic location, skills, work experience, internal equity, market conditions, education/training, and other factors, as reasonably determined by the University.

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