[ES_JOBS_NET] Faculty Research Assistant, causes and consequences of landscape change, Oregon State University
christin at ucar.edu
Sun Jan 25 09:09:09 MST 2015
Faculty Research Assistant
Faculty Research Assistant
This position requires a clear and unambiguous commitment to compliance
of all National Collegiate Athletic Association (NCAA) regulations for
Division I (FBS) universities.
Academic Teaching/Research Faculty
Earth, Ocean & Atmo Sci 261000 OAS
The College of Earth, Ocean, and Atmospheric Sciences invites
applications for a full-time (1.00 FTE), 12-month, Faculty Research
Assistant position. Reappointment is at the discretion of the Dean.
This Faculty Research Assistant (FRA) will aid in research by extracting
an understanding of the causes and consequences of landscape change from
large geospatial datasets. The position will be heavily focused on
computational methods in order to manage and manipulate time-series of
remotely sensed imagery; analyze relationships and trends in those
datasets through application of machine learning and statistical
approaches; and research and explore appropriate approaches to visualize
and distribute relevant findings. The FRA will both implement existing
algorithms to develop geospatial estimates of landscape change, and
develop improved and novel algorithms to describe change. Additionally,
the FRA will build from existing machine-learning and web-based
approaches to model the causes of landscape changes both in the western
United States (U.S.) and globally. Finally, this position will
contribute to summary analyses to address underlying science questions
related to landscape change.
The College of Earth, Ocean, and Atmospheric Sciences (CEOAS) is
internationally recognized as a leader in the study of the Earth as an
integrated system. It operates cutting edge computing facilities,
laboratories, and teaching facilities. The College has an annual budget
of more than $50 million, with much of the research support coming from
the National Science Foundation, National Oceanic and Atmospheric
Administration, National Aeronautics and Space Administration and other
federal agencies. It has approximately 104 faculty, 220 graduate
students and 600 undergraduate students. Graduate programs include
Master's and PhD degrees in Ocean, Earth and Atmospheric Sciences;
Geology; and Geography; and a Master's degree in Marine Resource
Management. The new undergraduate program in Earth Science, together
with the Environmental Sciences Undergraduate Program, provide
educational and research opportunities for the best undergraduate
students, a national honors college for the Earth.
CEOAS is a major participant in the three signature areas of Oregon
State University's strategic themes: Advancing the Science of
Sustainable Earth Ecosystems; Improving Human Health and Wellness; and
Promoting Economic Growth and Social Progress.
25% Data and Computing Management
Work with the Principal Investigator (PI) and others in the lab to
develop, implement, and maintain a productive computing environment for
lab research. Use shell and Python scripts to manage >80Tb of
mission-critical data on a Linux server and distributed desktop
workstations, maintaining organizational structure to allow automation
of algorithm implementation. With the support of college computing
facilities, maintain integrity of the platform including basic software
installations and user management. Maintain remote login capabilities
and management of Virtual Network Computing (VNC) connections.
45% Image Processing and Map Development
Implement and improve existing LandTrendr algorithms for development of
disturbance maps, land-cover maps, biomass maps, and maps of change
agent attribution for large areas of the western U.S. and abroad.
Explore and test relationships in these maps using Interactive Data
Language (IDL), Python and R to manipulate Landsat and other imagery
according to best image processing practices. Work fluently with
Geospatial Abstraction Data Library (GDAL) packages for manipulation of
spatial data as necessary. Contribute to development of novel processing
algorithms, and work to scale up such novel algorithms to very large
datasets. Contribute to development of cloud-based implementation of
these algorithms on Amazon Web Service (AWS) and Google EarthEngine
30% Data Analysis, Summary, Reporting and Distribution
Through Python, R, or other suitable open-source packages, work with the
lead scientist to develop robust statistical analyses of spatial and
temporal patterns of landscape change. Use appropriate statistical tools
to link these with a diversity of geospatial data (climate data,
environmental data, socioeconomic data) to explore/analyze causes and
consequences of landscape change. Work with the lead scientist to
interpret results and develop tabular and graphical format reports
suitable for publication. Develop web-based tools for outside users to
access reports and data, including File Transfer Protocol (FTP) site
maintenance and possible web-based map serving. Contribute to the
preparation of reports and data analysis for use in peer-reviewed
publications. May make presentations at scientific symposiums or
Position Duties (continued):
Working Conditions/Work Schedule:
Majority of work will be in a typical office environment. Will work, as
needed, in computing lab environment.
Bachelor's degree in geographical, computational, statistical,
mathematical, economic, ecological, environmental sciences or related
field and direct experience (6 months or more) in computational methods
for scientific research in the field.
Familiarity in application of statistics (univariate and/or
multivariate) through scripting.
Programming experience and demonstrated ability to learn and implement
new programming tools quickly.
Familiarity with script- and command-line manipulation of datasets and
file systems, including Linux.
A creative problem-solving nature; ability to learn and integrate new
concepts quickly; comfort applying solutions computationally; strong
work ethic and ability to work independently; good oral and written
communication skills; attention to detail; and demonstrated ability to
maintain a positive attitude.
Preferred (Special) Qualifications
Strong background in Python, GDAL, and/or R.
Theoretical background in remote sensing, including basic coursework in
Direct work experience processing optical satellite imagery and applying
multivariate algorithms to image datasets, particularly for time-series
Experience with data mining and machine learning algorithms including
random forest and similar approaches, preferentially implemented in the
R scripting language or in Python.
Demonstrated understanding of sampling theory in statistics.
Demonstrated understanding of land cover dynamics and drivers.
A demonstrable commitment to promoting and enhancing diversity.
Scholarly Outcomes for Position (academic faculty only)
Indicate how you intend to recruit for this search:
Competitive / External - open to ALL qualified applicants
For Full Consideration Date
Recommended Full-Time Salary Range
Salary is commensurate with education and experience.
A demonstrable commitment to promoting and enhancing diversity is:
A preferred qualification
Special Instructions to Applicants
When applying you will be required to attach the following electronic
1) A resume/CV that includes the names of at least three professional
references, their e-mail addresses and telephone contact numbers (Upload
as 'Other Document' if not included with your resume/vitae).
2) A cover letter indicating how your qualifications and experience have
prepared you for this position.
3) One example of written work where the applicant is first author,
either as a published work or from a class (Upload as Other Document 2).
Inquiries about the position may be directed via email to Dr. Robert
Kennedy, rkennedy at coas.oregonstate.edu.
Please visit http://ceoas.oregonstate.edu/ for information about the
College of Earth, Ocean, and Atmospheric Sciences.
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