EPA Fellowship on Applying Bayesian Networks to Wildfire Stream Health Assessments

Organization
U.S. Environmental Protection Agency (EPA)
Reference Code
EPA-ORD-CESER-LRTD-2024-02
How to Apply

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A complete application consists of:
  • An application
  • Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted. All transcripts must be in English or include an official English translation. Click here for detailed information about acceptable transcripts.
  • A current resume/CV, including academic history, employment history, relevant experiences, and publication list
  • Two educational or professional recommendations. Click here for detailed information about recommendations.

All documents must be in English or include an official English translation.

Application Deadline
8/16/2024 3:00:00 PM Eastern Time Zone
Description

*Applications may be reviewed on a rolling-basis and this posting could close before the deadline. Click here for information about the selection process.

EPA Office/Lab and Location: Two research opportunities are currently available at the Environmental Protection Agency (EPA), Office of Research and Development (ORD), Center for Environmental Solutions and Emergency Response (CESER), Land Remediation and Technology Division (LRTD) located in Cincinnati, Ohio or Corvallis, Oregon.

Research Project: This research project will utilize Bayesian networks to predict effects of wildfire on salmon habitat and populations in the western U.S. The research would develop conceptual and/or quantitative Bayesian networks to predict changes in impacts on model salmonid systems from climate-related disturbances. Ecological impacts would be modeled based on climate driven changes in temperature, precipitation and wildfire probability and severity. Intervention scenarios such as fuels treatments and stream restoration may be modeled to allow assessment of potential risk reduction and mitigation strategies. Model scenarios may also potentially facilitate assessments of vulnerabilities and resilience including habitats, species, and timing (e.g, frequency, season). Scenario model explorations will potentially allow identification of adaptive management triggers/thresholds.

Learning ObjectivesUnder the guidance of a mentor, research participant training activities may include:

  • Learning how to apply Bayesian networks to multi-media ecological risk assessment problems involving cascading impacts
  • Learning how to frame climate change problems
  • Learning how to quantify uncertainty and consider uncertainty in adaptive management planning
  • Learning how to develop a Quality Assurance Project Plan following EPA guidelines and good laboratory practice
  • Participating in conferences and workshops to present research results

Mentor(s)The mentor for this opportunity is John Carriger (Carriger.John@epa.gov). If you have questions about the nature of the research please contact the mentor(s).

Anticipated Appointment Start Date: Spring/Summer 2024.  All start dates are flexible and vary depending on numerous factors. Click here for detailed information about start dates.

Appointment LengthThe appointment will initially be for one year and may be renewed up to three or four additional years upon EPA recommendation and subject to availability of funding.

Level of ParticipationThe appointment is full-time.

Participant StipendThe participant will receive a monthly stipend commensurate with educational level and experience. Click here for detailed information about full-time stipends.

EPA Security ClearanceCompletion of a successful background investigation by the Office of Personnel Management (OPM) is required for an applicant to be on-boarded at EPA.

ORISE InformationThis program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and EPA. Participants do not become employees of EPA, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE.

ORISE offers all ORISE EPA graduate students and Postdocs a free 5 year membership to the National Postdoctoral Association (NPA).

The successful applicant(s) will be required to comply with Environmental, Safety and Health (ES&H) requirements of the hosting facility, including but not limited to, COVID-19 requirements (e.g. facial covering, physical distancing, testing, vaccination).

Questions: Please see the FAQ section of our website. After reading, if you have additional questions about the application process please email ORISE.EPA.ORD@orau.org and include the reference code for this opportunity.

Qualifications

The qualified candidate should have received a master's or 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 the past three years.

Preferred Skills/Experience:

  • Required:
    • Background in modeling, with an emphasis in environmental applications
    • Equally comfortable with working independently and in a team
  • Desired:
    • Background in probabilistic modeling, such as Bayesian Networks
    • Background in salmon ecology
    • Background in climate change, wildfire, hydrology, and/or ecosystem modeling
    • Background in causal modeling including causal model development
    • Equally comfortable working as a member of a research team and as an independent researcher
Eligibility Requirements
  • Citizenship: U.S. Citizen Only
  • Degree: Master's Degree or Doctoral Degree received within the last 36 months or currently pursuing.
  • Academic Level(s): Graduate Students, Postdoctoral, or Post-Master's.
  • Discipline(s):
    • Chemistry and Materials Sciences (1 )
    • Computer, Information, and Data Sciences (3 )
    • Earth and Geosciences (21 )
    • Engineering (1 )
    • Environmental and Marine Sciences (7 )
    • Life Health and Medical Sciences (4 )
    • Mathematics and Statistics (4 )
    • Other Non-Science & Engineering (2 )
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