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 PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.

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

The work will entail:
We propose to develop physics- and empirically informed digital twin representations for high-impact SI realizations of mass, voltage, and resistance that are advancing toward commercialization. Leveraging recent advancements in artificial intelligence (AI), including neural networks and digital twins, we expect to bypass the conventional, iterative prototyping and development cycle. Rather than taking decades to achieve an optimal design, our digital twin representations will enable rapid, offline optimization of their respective real‑life embodiments, significantly accelerating time to market.

U.S. Citizen Preferred

• Exploring AI networks for regression models of physical SI measurements
• Exploring 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 convolutional neural networks
• Ability to build deployable complex software solutions for image reconstruction
• Strong oral and written communication skills and 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. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate’s geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Johns Hopkins offers a total rewards package that supports our employees’ health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/

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