A complete application consists of:
All documents must be in English or include an official English translation.
If you have questions, send an email to EPArpp@orau.org. Please include the reference code for this opportunity in your email.
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.
In collaboration with the mentor and collaboration with a team of EPA scientists, the participant's research may include the following activities:
This program, administered by ORAU through its contract with the U.S. Department of Energy to manage the Oak Ridge Institute for Science and Education, was established through an interagency agreement between DOE and EPA.
The appointment is full time for one year and may be renewed upon recommendation of EPA and contingent on the availability of funds. The participant will receive a monthly stipend. Funding may be made available to reimburse the participant’s travel expenses to present the results of his/her research at scientific conferences. No funding will be made available to cover travel costs for pre-appointment visits, relocation costs, tuition and fees, or participant’s health insurance. The participant must show proof of health and medical insurance. The participant does not become an EPA employee.
The mentor for this project is Nicolle Tulve email@example.com. The desired start date is November 1, 2017.
Applicants must have received a doctoral degree in environmental sciences, environmental engineering, exposure science, environmental epidemiology, public health, or a related field within five years of the desired starting date, or completion of all requirements for the degree should be expected prior to the starting date. Experience working with: 1) high-resolution mass spectrometry data (e.g., LC-TOF, LC-QTOF, LC-OribTrap); 2) software programs for processing mass spectrometry data (e.g., Agilent MassHunter, ProFinder, and MPP; XCMS); and 3) statistical analysis programs/data processing packages (e.g., SAS, R, Python), is desirable.