"Postdoctoral Fellow (PREP0003665)"

Job Details

Johns Hopkins, founded in 1876, is America's first research university and home to nine world-class academic divisions working together as one university.

Salary: $75,000-$82,000 a year

Johns Hopkins University: Whiting School of Engineering: Commercial and Government Programs Office

Description

PREP Research Associate
CHIPS Funded Project.

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires that such institutions be the recipients of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.

Research Title:

Coupling Computation and Machine Learning to evaluate PFAS Chemicals

The work will entail:

The position is for a postdoctoral associate level researcher interested and experienced in applied data science to work jointly with chemists, mathematicians, and computer scientists at NIST to participate in the characterization and modeling of properties and spectra of PFAS chemicals with the goals of improving detection of PFAS compounds, replacing PFAS in plasma etching processes, or identifying solid adsorbent additives to remove PFAS. To accomplish these goals, the candidate will participate in the development of AI/ML algorithms for the prediction of chemical properties, infrared and mass spectra, and ionization cross sections, incorporating uncertainty quantification into AI/ML models and providing useful information in aid of UQ in AI/ML models to discern patterns in physical properties. The position will be highly interdisciplinary, requiring regular communication between chemists, computer scientists and mathematicians working on modelling these compounds using experimental as well as data from chemistry/physics calculations/simulations.

Key responsibilities will include but are not limited to:

• Developing novel machine learning algorithms for the prediction of physical and chemical properties, infrared and mass spectra, and ionization cross sections using data derived from experiment and computation.
• Implementing algorithms to study the performance of AI/ML classification models.
• Assessing uncertainty in prediction and classification of experimental data as well as data sets derived from quantum chemistry and physics calculations and simulations.
• Computationally testing mathematical and machine learning models with respect to accuracy and uncertainty quantification.
• Developing software to implement the goals stated above (most likely in Python).
• Disseminating results through posters/seminars at international meetings and university seminars.
• Ensuring that all results, findings, data, software, etc. are correctly archived and transmitted through appropriate channels.
• Attending regular meetings to present updates on research and to discuss progress with collaborators.

Qualifications

• Completed a PhD (or close to completion) in data science or related field.
• Knowledge of or a desire to acquire knowledge about chemistry.
• Minimum of 1 year of experience conducting data science research.
• Significant course work in one or more of chemistry, physics, mathematics, statistics and/or computer science.
• Familiarity with one or more AI/ML software packages. Familiarity with relevant, domain-specific software packages is preferred but not required.
• Ability to program in a modern computational language (e.g. Python).

Application Instructions

Please upload the following with your application:

• CV/Resume

Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.

• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender