[R-sig-Geo] Computational Ecologist Job at NOAA in Silver Spring, MD -- Marine Wildlife Spatial Modeling in R

Brian Kinlan (NOAA Affiliate) brian.kinlan at noaa.gov
Mon Apr 8 21:23:37 CEST 2013

Job ad- Please forward to all relevant departmental listservs and any 
interested colleagues!
The NOAA National Centers for Coastal Ocean Science is hiring a
Computational Ecologist, a statistical/computational ecologist with
experience fitting advanced spatial models to marine wildlife survey
data (e.g., seabirds and marine mammal transects, fisheries trawl
surveys) in R and other statistical languages. This is a full-time,
long-term stable contract position. We are looking for an expert R
programmer with experience in spatial modeling, especially of marine
wildlife survey transect data (e.g. seabirds, marine mammals). Please
note that this is a contract position, so rather than applying directly
to NOAA the link below directs you to the contracting company
(CSS-Dynamac, Inc.). We are looking to hire someone immediately.


Computational Ecologist

Contract position with NOAA's National Ocean Service, National Centers
for Coastal Ocean Science, Biogeography Branch (Contract Company:

Apply for this job online at

A person with experience or academic training in quantitative ecology,
advanced statistical modeling, computational analysis, and scientific
programming in R and Matlab; who also has demonstrated interest and
experience in advanced spatial analysis, is being sought for a full-time
contract position with the National Oceanic and Atmospheric
Administration’s (NOAA) National Centers for Coastal Ocean Science
(NCCOS). NCCOS’ Biogeography Branch conducts ecological and
oceanographic studies to map, characterize, assess, and model the
spatial distributions and movements of marine organisms across habitats
throughout the United States and Island Territories. We are seeking an
individual with a broad suite of quantitative, statistical, and
computational skills. A strong background in statistical modeling of
spatial ecological data with some experience in marine sciences is
preferred. The successful candidate will join an experienced scientific
team at the forefront of marine ecological predictive analytics. The
initial assignment for this position will involve developing,
implementing, and running machine-learning models for predictive
spatio-temporal modeling of marine bird and groundfish distributions to
support marine planning processes. Additional potential projects include
predictive modeling of deep sea corals, marine mammals, sea turtles,
marine fish, fishing fleets, and marine ecosystem processes. 

Core responsibilities
Provide statistical, computational, and analytical support to projects
that use predictive statistical models, in conjunction with large
wildlife survey and oceanographic databases, to provide
spatially-explicit maps and analyses that answer questions of marine
management and conservation relevance.
Design and implement spatial and spatio-temporal statistical models of
marine species’ distributions (e.g., seabird and marine mammal
occurrence probability and abundance), marine habitat, and marine
ecosystem properties
Develop and maintain computer code to interface with large oceanographic
and ecological databases and mine these databases to improve predictive
Assess model performance and uncertainty in management-relevant scenarios
Assist with writing journal articles/reports and present at scientific
Offer technical guidance for selection and implementation of different
statistical methods to detect patterns in wildlife surveys.
Explain statistical results as they relate to project goals and
summarize results in the form of tables, figures, journal articles and
technical reports.
Travel to federal and state laboratories and academic institutions as
part of collaborative research projects
Develop, maintain, and grow a codebase for advanced spatial analysis
Apply new developments in statistical modeling to a marine
ecological/wildlife survey context
Implement model selection, assessment, and validation algorithms
Develop, maintain, and grow oceanographic and ecological geo-databases
Build a database of oceanographic and environmental predictor variables
of relevance to marine ecological modeling
Analyze satellite and observational datasets and raw ocean model outputs
to develop derived products that improve predictive models
Automate data acquisition, data mining, model assessment & QA/QC processes

Advanced degree (Masters or PhD), or equivalent experience, in
Quantitative Ecology, Applied Statistics, or similarly highly
quantitative field. Ecology, Marine Science and related advanced degrees
also acceptable with demonstrated evidence of a strong quantitative
focus and statistical and computer programming expertise described below
Must be proficient and highly experienced with R and Matlab (3-5+ years
experience with one or both of these languages); a code sample may be
requested to demonstrate proficiency
Experience implementing a variety of spatially-explicit statistical
models in R and/or Matlab, including at least 3 of the following:
machine learning models (e.g., component-wise boosting), geostatistical
models, GLMMs, GAMs, regression trees/forests
Ability to independently identify, analyze and solve complex statistical
and computational problems
Demonstrated written and oral scientific communication skills
Able to work effectively in a dynamic, fast-paced, team-oriented
multi-project environment

Experience with spatial analysis of wildlife survey data, especially
marine bird data, in the marine environment
Knowledge of ecology, marine science, oceanography, and/or a related field
Ability to interface with large databases through THREDDS/ERDDAP servers
in R and/or Matlab
Experience working with ocean remote sensing data, numerical ocean model
outputs (e.g., ROMS), and large distributed oceanographic databases
Proficiency with programmable GIS (e.g., Python scripting with ArcGIS or
equivalent); Experience with geostatistics (gstat, ESRI Geostatistical
Analyst, rgeos, GSLIB, or equivalent)
Although not required, we value experience developing hierarchical
Bayesian or Approximate Bayesian models on large spatial datasets
Record of academic publication

Apply for this job online at
To discuss the position or for more information contact:
Dr. Brian Kinlan
Marine Spatial Ecologist
NOAA NOS National Centers for Coastal Ocean Science
Brian.Kinlan at NOAA.gov

Brian P. Kinlan, Ph.D
Marine Spatial Ecologist

NOAA National Ocean Service
Contractor, CSS Inc.
NCCOS-CCMA-Biogeography Branch
1305 East-West Hwy, SSMC-4, N/SCI-1, #9224
Silver Spring, MD 20910-3278


Disclaimer: Any views or opinions expressed in this message are those of
the sender, not NOAA, the United States Government or its agents, or
Consolidated Safety Services, Inc.

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