Postdoctoral Fellow; PREP

PREP Research Associate

CHIPS Funded Project

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). It supports a collaboration with academic institutions on specific projects of mutual interest. Individuals in this role will perform technical work supporting the collaboration's scientific research.

Postdoctoral Researcher Applying Machine Learning Methodologies to Predict Spectra of PFAS

U.S. Citizen Preferred

• Develop libraries of training data through mining of existing databases and simulation of infrared and mass spectra using quantum chemistry and related methodologies.
• Create AI/ML models for high‑fidelity prediction of infrared and mass spectra and validate them against experimentally measured spectra.
• Collaborate with other computational and experimental researchers to meet project goals.
• Disseminate results through publications, talks, poster presentations, and other scientific communications.

• Ph.D. in chemistry, physics, or a closely aligned field.
• Demonstrated experience in conducting quantum scattering calculations.
• Strong programming skills in Python or C/C++, experience with modern AI/ML software frameworks, and data analysis.
• Motivated, independent researcher with good organizational, communication, and leadership skills.
• Solid track record of scientific publication.

The referenced salary range represents the minimum and maximum salaries for this position. Not all candidates will be eligible for the upper end. The actual compensation offered may vary and will depend on factors such as location, skills, experience, internal equity, market conditions, and education/training.

Johns Hopkins offers a total rewards package that supports employee health, life, career, and retirement. More information can be found at .

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.

After receiving a conditional offer, successful candidates will be subject to a pre‑employment background check, including education verification. Conviction history is considered based on nature, gravity, time elapsed, and job relevance.

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