Assistant Research Scientist; PREP

Johns Hopkins, founded in 1876, is America's first research university and home to nine world-class academic divisions working together as one university.

$62,500 - $70,000

Johns Hopkins University:
Whiting School of Engineering:
Office of Research and Translation

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.

PREP Researcher associate will continue to conduct research on failure analysis and degradation mechanism study of polymer exposed to accelerated laboratory testing environments and operation conditions. Understanding the degradation mechanism of polymeric components used in immersion cooling during services is critical to the development and assurance of AI physical Infrastructure. In this study, non-destructive and destructive techniques will be developed for the failure analysis and degradation mechanism of polymeric components in aged samples after accelerated multi-stress testing.

The mechanisms of chemical, physical, and mechanical degradation will be studied using spectroscopic, microscopic, and mechanical techniques, such as attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), micro-IR, laser scanning confocal microscopy, and tensile tester. The results will be used to understand the root causes of polymer failure, providing a scientific basis for test method development, material selection, and product quality assurance AI physical Infrastructure.

PREP Researcher associate will continue to conduct research on failure analysis and degradation mechanism study of polymer exposed to accelerated laboratory testing environments and operation conditions. Understanding the degradation mechanism of polymeric components used in immersion cooling during services is critical to the development and assurance of AI physical Infrastructure. In this study, non-destructive and destructive techniques will be developed for the failure analysis and degradation mechanism of polymeric components in aged samples after accelerated multi-stress testing.

The mechanisms of chemical, physical, and mechanical degradation will be studied using spectroscopic, microscopic, and mechanical techniques, such as attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR), micro-IR, laser scanning confocal microscopy, and tensile tester. The results will be used to understand the root causes of polymer failure, providing a scientific basis for test method development, material selection, and product quality assurance AI physical Infrastructure.

• Designing and developing a testbed for accelerated multi-stress testing of materials used in immersion cooling systems, including thermal, chemical, mechanical, electrical, and hygrothermal cycling.
• Conducting chemical, physical, and mechanical characterization of polymer components used in immersion cooling systems during accelerated multi-stress testing.
• Evaluating the long-term durability and reliability of immersion cooling systems and developing service-life prediction models for various polymer components.
• Analyzing and interpreting experimental data; presenting research findings; and preparing test protocols, technical standards, reports, and peer-reviewed journal publications.

• U.S. Citizenship preferred.
• A master’s degree in materials science and engineering, mechanical, or chemical engineering background, with at least 3 years of relevant professional experience.
• 3+ years of research experience in multilayer failure analysis, surface/interface characterization, and polymer durability assessment.
• 3+ years of experience applying machine learning, deep learning, and other AI methods to polymer degradation and service-life prediction.
• Extensive hands‑on experience with characterization of multilayer polymeric materials, including microscopy (confocal…