Position Description

Occupational Summary

The Marine Geospatial Ecology Lab (MGEL) at Duke University seeks a Postdoctoral Associate or Research Scientist (depending on level of experience) in marine spatial ecology for immediate hire to model species distributions of marine mammals. Directed by Dr. Patrick Halpin, MGEL is a leader in applying geospatial analysis to problems in marine ecology, wildlife management, and ocean conservation. We are seeking a highly motivated individual who is interested in collaborative modeling of marine mammal species distributions, with a focus on developing management-ready results and accompanying manuscripts suitable for immediate application to ongoing protected species management problems and environmental planning activities in the United States. This is a two-year position, with extension beyond that contingent upon evaluation and funding.  The position may be located on-site at Duke University in Durham, NC, at the Duke University Marine Lab in Beaufort, NC, or off-site/remote as permitted under university policy. Salary and job title commensurate with degree/experience. 

The successful candidate will join a small team that models spatiotemporal distributions of marine species and applies the results to management problems throughout the world. The work will involve bouts of collaborative research and problem solving with other team members interspersed with periods of extensive independent, candidate-led modeling and development. The candidate will be expected to travel to scientific conferences and meetings to present and discuss results of the research, to keep abreast of developments in the field, and to author and submit publications to peer-reviewed scientific journals. This position is best suited to those who enjoy statistics, coding, manipulating and visualizing data, and other quantitative analysis tasks.

Work Performed

The new researcher will leverage knowledge and skills from a number of fields, including marine mammal ecology and biology, statistical modeling, oceanography, and scientific programming and data processing to model species distributions for several motivating management applications. The taxonomic focus will be marine mammals, particularly cetaceans, with a special emphasis on the North Atlantic right whale. The primary task will be to develop density surface models from visual line transect surveys conducted by a large number of collaborating institutions along with gridded environmental covariates derived from satellite remote sensing, ocean models, and distribution models of prey species.

Specific primary tasks include: cleaning and integrating line transect surveys into a common database; modeling species detection probability with distance sampling; selection, acquiring, reformatting, and sampling gridded environmental data for use as model covariates; creating species density surface models, typically in R with mgcv-based generalized additive models; predicting density and uncertainty surfaces from those models; evaluating predictions with contemporaneous data not included in the models as well as knowledge from the literature; and communicating results effectively in presentations and manuscripts. All projects will require regular interaction with internal and external collaborators and funders. The candidate must be comfortable and effective in leading discussions about project status and current results in audio and video calls and in-person meetings, and communicating the same in formal and informal written reports.

The ideal candidate will be able to carefully reason about what inference can be gained through the joining and modeling of diverse datasets. This will include investigating how well models may be transferred to unsurveyed areas, seasons, or conditions and designing models to maximize transferability. The researcher must be comfortable working with imperfect sampling designs and heterogeneous coverage, and be prepared to deal with strong spatial and temporal biases in survey effort. Some degree of extrapolation will almost always be required, and the researcher must decide what is acceptable and be prepared to defend their choices with evidence.

Specific projects depend on MGEL team needs and researcher experience but are likely to include:

1. Projecting Changes to North Atlantic Right Whale Density in the 21st Century and the Future Risk of Overlap with U.S. Lobster Fisheries. A persistent threat to the endangered North Atlantic right whale (Eubalaena glacialis) is entanglement in buoy lines in trap and pot fisheries, such as those for American lobster (Homarus americanus). In the United States, the densest aggregations of lobster buoy lines occur in the Gulf of Maine, which is also an important feeding area for the right whale. The rapid warming of the Gulf of Maine has been linked to distribution shifts in both species. For this project, the researcher will collaborate with a multi-institution research team that is investigating how continued warming might drive further changes in upcoming decades, and what effects this might have on entanglement risk in the future. The new researcher’s primary role will be to lead development and publication of a model that forecasts future right whale density distribution from ocean climate models, and then collaborate with the wider team on models of future whale, lobster, and fishery overlap.
2. Marine Mammal Density Models for the U.S. Navy Atlantic Fleet Testing and Training Study Area. Under the U.S. Marine Mammal Protection Act (MMPA), the U.S. Navy is responsible for assessing potential impacts of peacetime testing and training activities to marine mammal populations every seven years. To facilitate this, as well as other environmental planning processes, the Navy initiated a cooperative research agreement with MGEL to develop new density surface models for all marine mammal species along the east and Gulf coasts of North America, as well as a wider study area that extends east to longitude 45°W. For this project, the researcher will lead the development of a portion of the full suite of models developed for the Navy’s needs, collaborating closely with other MGEL staff who are similarly working on a different package of models. While project #1 will focus on a single high-priority species in a forecasting scenario, project #2 will focus on a larger number of species in a more traditional hindcasting scenario, with an emphasis on obtaining a set of consistent, high-quality models that are equally suitable for subsequent population impact analysis under the MMPA by the Navy and other interested parties. This project will culminate with release of the models to the Navy as well as to the public online, with opportunities for the new researcher to lead or coauthor resulting publications.
3. Advanced Models and Modeling Methods. While progressing on core modeling projects such as those above, the new researcher may also participate in ongoing and upcoming projects that involve less developed, more experimental models and modeling methodologies. These may include: methods for integrating visual and passive acoustic surveys into a single model; methods for developing and actual development of density models that span the entire North Atlantic basin, addressing the problems of disparate survey data, geographically-differing species-habitat relationships, and large unsurveyed expanses that require substantial extrapolation; and a system that issues nowcasts and short-term density forecasts for North Atlantic right whale in near real-time.

Term

One year initial appointment with potential for annual renewal for a total two-year term with extension beyond that contingent upon evaluation and funding.

Work Location

The position may be located at Duke University in Durham, NC, the Duke University Marine Lab in Beaufort, NC, or off-site/remote as permitted under university policy. Salary and job title commensurate with degree/experience.

Required Qualifications at this level

Education / Training

Academic credentials: PhD in ecology, biology, statistics, computer science or engineering, or oceanography with a strong quantitative analysis background, particularly in species distribution modeling. Specific coursework and research experience with marine mammals strongly preferred. Undergraduate coursework or equivalent experience in physical and biological oceanography is required; graduate-level coursework or research experience preferred.

Experience

This position requires a minimum of two years of direct work experience, or academic equivalent.

Skills

We are seeking candidates with both a mix of established skills and a strong aptitude to learn new skills.

Communication

·         Fluent in English (reading, writing, and speaking).

·         Proficient in giving scientific presentations, with demonstrated experience.

·         Strong scientific writing skills, in English; record of peer-reviewed publications preferred.

Teamwork

·         Competent working in a team on joint projects, both face-to-face and remotely.

·         Also able to work independently, with only occasional oversight.

·         Comfortable and competent interacting with external collaborators, funders, and stakeholders.

·         Capable of working on multiple projects simultaneously.

·         Able to complete tasks on time and meet hard deadlines imposed by funders, regulatory processes, or other external sources.

Analysis and Modeling

·         Strong background and competency in mathematics (undergraduate calculus, at minimum). Graduate-level training or experience is a plus.

·         Strong proficiency in statistical modeling, including regression and classification modeling. Experience with GLMs and GAMs strongly preferred.

·         Demonstrated experience with species distribution modeling and/or abundance estimation, including habitat suitability modeling, distance sampling, occupancy modeling, capture-recapture methods, etc. Specific experience with distance sampling and density surface modeling strongly preferred.

·         Strong programming skills, including proficiency in R (or equivalent with ability to quickly become proficient in R). Proficiency with Python, MATLAB, and SQL a plus.

·         Demonstrated competence with GIS, geospatial analysis, and mapping. Ability to automate production of maps and other geospatial operations preferred.

·         Competent at working with multiple tabular data formats, including CSV and other text formats, and relational databases (MS Access, SQL, etc.).

·         Proficient in summarizing/aggregating, transforming, and joining tabular data in R and/or SQL.

·         Proficiency utilizing large language models effectively to aid research is a plus. Experience with agentic programming is a plus. However, these and other AI technologies are no substitute for unassisted skill, logical thinking, attention to detail, scientific discipline, deep understanding, precise communication, and original creativity.

Additional Information:

Duke University is an 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, (including pregnancy and pregnancy related conditions), sexual orientation, or military status.

Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas-an exchange that is best when the rich diversity of our perspectives, backgrounds, and experiences flourishes. To achieve this exchange, it is essential that all members of the community feel secure and welcome, that the contributions of all individuals are respected, and that all voices are heard. All members of our community have a responsibility to uphold these values.

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