Description:
Research Project:Â The focus of this research training opportunity is remote sensing, specifically:
- development and application of mixture density networks (MDN), neural network approaches to predict/map chlorophyll concentrations; and
- development of predictive models for cyanobacterial blooms, both within estuaries and freshwater tidal rivers of the United States, using remote sensing images (e.g., Sentinel 2) as inputs.
The tools to be used include an extensive EPA database of paired chlorophyll/remote sensing reflectances with ancillary explanatory variables, USGS Earth Explorer, Google Earth Engine, toolboxes for atmospheric correction (e.g., ACCOLITE, POLYMER, SIAC), R and Python programming languages, an existing MDN Python toolbox for neural network analysis, and supercomputer access if needed.
The project integrates remote sensing, programming, and applied statistics.
The research participant may be involved in the following research activities:
- developing and applying approaches for bulk download of remote sensing images
- application of toolboxes for atmospheric corrections
- application of MDN methods for chlorophyll prediction, and
- development of logistic models to predict cyanobacteria metrics.
Learning Objectives: The research participant may learn about atmospheric corrections for Sentinel 2 data, managing and analyzing large imagery datasets within Google Earth Engine, machine learning approaches for predicting chlorophyll a, and development of predictive logistic models for cyanobacteria. The research participant may author or co-author on peer-reviewed publications, and may present at local and national meetings (possibly virtually). The participant will be a member of a multi-disciplinary research team.
Mentor(s): The mentor for this opportunity is Steven Rego (rego.steven@epa.gov). If you have questions about the nature of the research please contact the mentor(s).
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.
Qualifications
The qualified candidate should have received a master’s or doctoral degree in one of the relevant fields, or be currently pursuing one of the degrees and will reach completion by the appointment start date. Degree must have been received within five years of the appointment start date.
Preferred skills:
- Strong written, oral and electronic communication skills
- Background and experience in remote sensing
- Proficiency in developing data science applications with R and/or Python
- Experience with Google Earth Engine
- Statistics, ideally including regression and machine learning approaches
- Experience with Arcgis or geospatial analysis with R
Eligibility Requirements
- Citizenship:Â U.S. Citizen Only
- Degree:Â Master’s Degree or Doctoral Degree received within the last 60 months or anticipated to be received by 3/31/2022 11:59:00 PM.
- Discipline(s):
- Veteran Status:Â Veterans Preference, degree received within the last 120 month(s).
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.
For further information please visit this website.