Assistant Research Scientist (PREP0004370)

Johns Hopkins University; Baltimore (MD); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

Comprehensive benefits package

Job summary

Johns Hopkins University is seeking a PREP Research Associate in Baltimore, MD. This role involves performing technical work in scientific research, focusing on AI-powered models for physical measurements. Ideal candidates are PhD candidates in Physics with expertise in platforms like PyTorch/Python.

The position offers opportunities to contribute to cutting-edge research while also receiving comprehensive employee benefits. Strong communication skills are essential for presenting findings.

Qualifications

• 2 or more years of relevant experience.
• Ability to build deployable complex software solutions for image reconstruction.

Responsibilities

• Exploring AI networks for regression models of physical SI measurements.
• Creating presentation material of the results.

Skills

PyTorch/Python

AI models

Strong communication skills

Education

PhD candidate in Physics

Job description

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.

Research Title

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

Key Responsibilities

• 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

Qualifications

• 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

Salary Range

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.

Total Rewards

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/

Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, 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 is committed to providing 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.