Duke University, Biology

3 15385
Position ID:Duke-Biology-EB_AINR [#15385]
Position Title: Associate in Research-Bernhardt Lab
Position Type:Other
Position Location:Durham, North Carolina 27708, United States [map]
Subject Area: Biology / Biochemistry
Appl Deadline:2020/05/31help popup (posted 2019/11/05)
Position Description:    

Data scientist wanted

We are hiring a data scientist or data-savvy environmental scientist to join macrosheds , a study of comparative ecosystem biogeochemistry at continental scales.

NSF Public abstract

This project will enable anyone with internet access to compare the flow and the chemistry of hundreds of streams throughout the United States and to explore their watersheds. It will combine data sets from many separate research projects into an attractive website that makes the data available. This will make it easy for scientists and students to generate questions about water quality and river flow patterns across the continent. Researchers will use these data to study what types of watersheds are best at retaining nutrients, are recovering most rapidly from decades of acid rain, have the highest erosion rates, and have flow patterns that are least sensitive to floods and droughts. The lessons we learn from studying many watersheds and streams will contribute to more effective management of our nation’s water and forest resources.

Much of the literature of watershed ecosystem science over the last decade has focused on gaining ever finer detail of spatial heterogeneity within watersheds. This fine-scale focus has identified many idiosyncrasies of individual watersheds but has not helped us develop general theories about watershed dynamics. Most watershed ecosystem studies remain rather parochial, involving detailed studies of individual or paired watersheds, or surveys of a small set of attributes across multiple watersheds. Macrosystem watershed science, or the search for general principles that describe the functional capacity and behavior across watersheds, has been limited. A major reason for this lack of large-scale focus is the challenge of data access and integration across sites. Our goal in this proposal is to compile a dataset that merges all US watershed ecosystem studies into a common platform ( macrosheds ) and to enable and train a new generation of watershed ecosystem scientists in the art and practice of macroscale watershed ecosystem science.

Candidate description

The central informatics goals of this project are to centralize and harmonize data (sensor time series, geographic data, metadata) from diverse sources, develop a cloud-based open data management and exploration platform, and allow users to access, clean, analyze, and visualize the data sets housed within it. The successful candidate will have interest and experience in one or more of the following disciplines: data engineering, analytics, data visualization, software development, GIS. A graduate degree in either data or computer science or in an environmental science is desired but not required. The position includes support for the candidate to attend professional meetings and professional training workshops and the opportunity to interface with collaborators at the National Ecological Observatory Network (NEON), Colorado State University, and Duke University.

Key tasks will include some subset of the following, depending on applicant’s skillset and interests:  Development of interactive web visualizations (Using one or more of: Shiny, D3, Dygraphs, Bokeh, Highcharts).  Development and scheduled execution of scripts to pull data from web APIs and FTP servers.  Data munging, cleaning, harmonization, and database I/O.  Programmatic collection and summarizing of geographic data.  Python web development (Flask).  Web scraping.

Ideal candidates will have experience with three or more of the following:  R  Python  any database query language  Mac/Linux shell commands (Bash), especially executed remotely  Git  HTML, CSS, JavaScript  Google Earth Engine (JS or Python versions)  Watershed terrain analysis

Who we are

The successful applicant will work closely with the project’s full-time data scientist, who will provide regular support and guidance. For an idea of what our web platform will look like and some of the visualizations it will provide, check out the demo.

Duke is an Affirmative Action / Equal Opportunity Employer committed to providing employment opportunity without regard to an individual’s age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status.


Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the position description.

Further Info:
www.biology.duke.edu
email
 
Box 90338, Durham, NC 27708

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