Assistant Research Scientist; PREP

PREP Research Associate

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). The researcher will support a collaboration’s scientific research by contributing technical work in AI and digital twin development for physical SI measurements.

SI meets AI:
Neural Network-powered Digital Twin for Advancing Primary Standards

• Explore AI networks for regression models of physical SI measurements.
• Explore AI networks for image classification of graphene data.
• Create presentation material of the results.

• US citizenship is preferred.
• PhD candidate in physics with 2 or more years of relevant experience.
• Expertise in PyTorch/Python and state‑of‑the‑art AI models such as vision transformers and advanced CNNs.
• Ability to build deployable complex software solutions for image reconstruction.
• Strong oral and written communication skills and strong presentation skills.

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. The actual compensation offered to the selected candidate may vary and will ultimately depend on a range of factors, including geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the university.

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. The university provides qualified individuals access to all academic and employment programs, benefits, and activities on the basis of demonstrated ability, performance, and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved.