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

Brian Kinlan (NOAA Affiliate) br|@n@k|n|@n @end|ng |rom no@@@gov
Mon Apr 8 21:21:07 CEST 2013

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 using 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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