Postdoctoral Fellow (PREP0003620)

Johns Hopkins University; Gaithersburg (MD); On-site; USD 70,000 - 90,000; Full time

Benefits offered by this job

Total rewards package including health and retirement benefits

Job summary

Johns Hopkins University is seeking a PREP Research Associate to apply machine learning methodologies for predicting spectra of PFAS compounds. The role requires a Ph.D. in a relevant field, strong programming skills in Python or C/C++, and experience in quantum scattering calculations. This position involves collaboration with researchers from multiple disciplines and aims to contribute to advancements in semiconductor materials. The ideal candidate will have a proven track record of scientific publication and effective communication skills.

Qualifications

• Demonstrated experience in conducting quantum scattering calculations.
• Motivated, independent researcher with good organizational, communication and leadership skills.
• Solid track record of scientific publication.

Responsibilities

• Develop libraries of training data using quantum chemistry methodologies.
• Create AI/ML models for high-fidelity prediction of spectra.
• Collaborate with other researchers to meet project goals.
• Disseminate results through publications and presentations.

Skills

Programming in Python or C/C++

Quantum scattering calculations

AI/ML experience

Data analysis

Organizational skills

Communication skills

Leadership skills

Education

Ph.D. in chemistry, physics, or a closely aligned field

Job description

PREP Research Associate

CHIPS Funded Project

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. 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

Postdoctoral Researcher Applying Machine Learning Methodologies to Predict Spectra of PFAS Compounds

The work will entail: The Materials Measurement Laboratory of the National Institute of Standards and Technology is seeking qualified persons (U.S. Citizens preferred) to apply modern methods in artificial intelligence (AI) and machine learning (ML) to the problem of predicting infrared spectra and mass spectra for PFAS compounds. The candidate should have a strong background in AI/ML with application to chemical problems, have familiarity with infrared and mass spectra, and understand the relevant chemistry of PFAS molecules. This position will involve working with a team of chemists, physicists, mathematicians, data scientists and machine learning experts characterizing PFAS molecules used in the semiconductor industry with the goal of discovering new molecules for the semiconductor etching process.

U.S. Citizen Preferred

Key Responsibilities

• 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 the infrared and mass spectra and validate their use in matching experimentally measured spectra.
• Collaborate with other computational and experimental researchers to meet project goals.
• Disseminate results through publications, talks, poster presentations, etc.

Qualifications

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

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 characteristic. 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 that are irrelevant to the program involved.

Background Checks

The successful candidate(s) for this position will be subject to a pre‑employment background check including education verification.