See below. Click here for a link to the job posting.
Application Deadline: 2/15/2022 3:00:00 PM ET
Citizenship: U.S. Citizen Only
Degree: Doctoral Degree received within the last 60 months or anticipated to be received by 1/31/2022 11:59:00 PM.
Research Project: Non-targeted analysis (NTA) methods based on high-resolution mass spectrometry (HRMS) are now widely used for identifying emerging organic contaminants in a variety of media. To date, most research emphasis has been placed on developing technologies to confidently identify the chemical structures of emerging contaminants. Considerably less emphasis has been placed on developing technologies to confidently quantify detected analytes in the absence of authentic chemical standards. Chemometric and statistical modeling approaches can now predict HRMS instrument response based on chemical structure, convert predicted instrument response to estimated analyte concentration, and calculate confidence intervals about spot concentration estimates. While these achievements mark great strides for the NTA field, more research is needed to operationalize and standardize quantitative NTA (qNTA) methods, and to significantly reduce predicted uncertainties. The research participant will be part of an EPA team of experts who are researching the best strategies to improve qNTA methods so that they are suitable for use by HRMS-equipped public health laboratories.
The participant will contribute to code development and utilization of statistical/mathematical/machine learning models for generating qNTA estimates. Research activities may include computer programming (Python and/or R), numerical verification of experimental data and code output, model selection, model execution, results evaluation, and written/oral discussion.
Qualifications: The qualified candidate should have received a doctoral degree in one of the relevant fields, or be currently pursuing the degree with completion by the appointment start date. Degree must have been received within five years of the appointment start date.
Preferred skills:
- Experience using a scientific programming language (e.g. R, Python) to summarize and manipulate data files, manage data, and conduct data analyses (including statistical and mathematical modeling)
- Evidence of coursework and/or experience performing quantitative chemical analysis, including a working knowledge of mass spectrometry
