Post-doctoral Researcher Multi-species fisheries stock assessment modeling NOAA Fisheries Northeast Fisheries Science Center, Woods Hole, MA
As part of developing scientific decision support tools for operationalizing ecosystem-based fisheries management (EBFM),
multi-species population models have become increasingly important as a tool to understand the effects of primary species on each other in marine ecosystems. Although a broad range of modeling approaches for EBFM have been suggested, multi-species assessment models that focus on ecologically or economically important stocks have the potential to provide tactical catch advice while accounting for species interactions and the effects of species composition on the productivity of commercially important stocks. Within the field of single-species population modeling, state-space approaches to inference have recently become more prominent and practical to implement. These models have separate components for stochastic processes related to the population changing over time and for the sampling that generates observations at particular points in time.
The PR will collaborate with Drs. Tim Miller and Kiersten Curti in the Population Dynamics Branch at NEFSC and Dr. Gavin Fay at the University of Massachusetts Dartmouth on a research project to develop and evaluate a state-space, age-structured, multi-species assessment model. The PR will develop and apply computer programs to fit these models to survey, catch, and diet data for a set of species in Georges Bank. Models will also be fit to simulated data to evaluate the statistical behavior and effects of model mis-specification on the estimates provided by the model, including the tradeoffs among management objectives associated with projecting models under alternative fishing scenarios. The computer programs developed by the PR will provide a general framework that can be applied to other sets of species and/or large marine ecosystems. The PR will take a leading role in the creation and publication of reports, peer-reviewed papers, and presentations at scientific meetings on the results of this research.
*Qualifications*
1. A completed (or nearly-completed), earned PhD degree in a relevant discipline, such as Fisheries Science, Statistics, Ecology, or other related field, that demonstrates a strong quantitative background.
2. Experience fitting population dynamics models to data for fisheries stock assessment is preferred.
3. Demonstrated experience of fluency in statistical/modeling programming languages (e.g. R, AD Model Builder, Template Model Builder).
4. Strong written and oral communication skills, as evidenced preferably through publications in the peer-reviewed scientific literature and presentations to a variety of audiences.
The position has an annual salary of $55K and is eligible for the benefits described on the Integrated Statistics website (http://integratedstatistics.com). This position is full-time for two years, with the second year of funding conditional on the PR making
satisfactory progress during the first year. Start date is flexible, and the successful candidate could begin as soon as possible.
The position will be located at NOAA Fisheries in Woods Hole, MA with some expectation for travel to scientific meetings and to meet with regional partners. Desk space will also be provided at the University of Massachusetts’ School for Marine Science and Technology and the PR encouraged to interact with students and staff at both collaborating institutions. Opportunities exist for teaching, student mentoring, academic training, and participation in regional scientific advisory working groups dependent on PR interests and career goals. Further information on the NEFSC and UMass Datmouth SMAST can be found via the institutions’ websites (www.nefsc.noaa.gov; www.smast.umassd.edu).
Applications will require a cover letter, CV, writing samples (e.g. copies of relevant publications), and contact information for at least
two professional references, and may be submitted here: http://jobs.intstats.com/JobDetails.jsp?jobListingId=72