Two postdoctoral positions in Research Triangle Park

There are two openings for postdoctoral positions in Research Triangle Park, with a desired start date in November 2017:

Using Non-Targeted Analysis Methods to Identify Ubiquitous and Unique Compounds in Dust


This research project will pursue research related to the analysis of large, laboratory-derived data sets to identify compounds that are both ubiquitous and unique to dust in order to be able to identify a list of candidate tracer compounds for use in field studies involving human participants.The participant's research may include the following activities:

  • Devise scientific approaches and investigations to better understand human exposure to environmental chemicals using laboratory measurement data.
  • Generate and evaluate lists of chemical features in dust samples using non-targeted analysis approaches.
  • Use existing high-resolution mass spectrometry data, software packages, and chemical databases to screen for emerging chemical contaminants in environmental (e.g., dust) samples.
  • Compare chemical features in dust with those measured in other environmental (e.g., soil) and biological media (e.g., serum).
  • Use lists of chemical features along with existing models, software tools, and cheminformatic approaches to identify specific chemical structures that are ubiquitous in dust, unique to dust, and likely measurable as a human exposure biomarker.

Metabolomics and Non-Targeted Analysis to Support High-Throughput Exposure Screening

A postdoctoral research project training opportunity is currently available at the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL) in Research Triangle Park, North Carolina. This training opportunity is with NERL’s Exposure Methods and Measurements Division (EMMD). This research project is in support of the EPA’s “Chemical Safety for Sustainability” (CSS) research program and will focus on utilizing high-resolution mass spectrometry to evaluate human exposure to environmental chemicals.

A major goal of the CSS research program is to understand the extent to which environmental chemicals may impact human and ecosystem health. Understanding the health risks posed by chemical stressors requires a quantitative understanding of both dose-response relationships and chemical exposure. Computational models now allow rapid predictions of exposure and dose across thousands of chemicals. However, data with which to evaluate model predictions are lacking. Targeted analytical methods alone are unable to meet the demands of high-throughput (HT) exposure and risk assessment. Efficient non-targeted methods are therefore needed to expand existing measurement domains. Data generated from these methods will be used to evaluate predictions from HT exposure and dose models, prioritize previously unmeasured chemicals for future HT testing, and evaluate co-occurrence of individual analytes in various environmental and biological media. This project will focus on developing, refining, and applying analytical techniques, based on high-resolution mass spectrometry platforms, in order to better understand human exposure to xenobiotic chemicals.

The participant may be involved in the following training activities:

  • Devising scientific approaches and investigations to better understand human exposure to environmental chemicals.
  • Using existing software packages, chemical databases, and high-resolution mass spectrometry to screen for emerging chemical contaminants in environmental (e.g., drinking water) and biological (e.g., blood) samples.
  • Using existing software packages and mass spectrometry data (i.e., exact mass, isotope patters, MS and MS/MS spectra) to examine molecular features identified in environmental and biological samples.
  • Implementing environmental degredation and human metabolism models to enhance existing non-targeted analysis methods.
  • Integrating results of non-targeted analyses and metabolomic experiments to understand the effects of chemical exposures on living systems.
  • Developing prediction models (for estimating retention time, chemical concentration, optimal method conditions, etc.) using cheminformatic and machine learning techniques.
  • Writing peer reviewed manuscripts and responding to peer review comments by scientists in NERL, other parts of EPA, and external reviewers.
  • Presenting research findings at national/international scientific meetings relevant to the research.