Postdoctoral Fellow (PREP0004388) Full-time

Postdoctoral Fellow

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.

The work will entail: The position is for a postdoctoral associate level researcher interested and experienced in applied mathematics, statistics or data science to work jointly with NIST research scientists and mathematicians to participate in a research project involving 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 mathematical, statistical and AI/ML algorithms for the prediction of chemical properties, infrared and mass spectra, and ionization cross sections, incorporating uncertainty quantification (UQ) into approaches and providing useful information in aid of 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 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 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 models with respect to accuracy and uncertainty quantification.
• Developing software to implement the goals stated above (most likely in Python or R).
• 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.

Qualifications:

• An advanced degree in a scientific/mathematical/statistical area.
• Familiarity with applied science and numerical work
• Ability to work with a multi-disciplinary research team
• Strong oral and written communication skills.