[MARMAM] Job opportunity: marine mammal density modeler

Jason Roberts jason.roberts at duke.edu
Tue Sep 29 07:19:54 PDT 2020

Dear all,

The Marine Geospatial Ecology Lab (MGEL) at Duke University seeks a Postdoctoral Associate for immediate hire to model spatiotemporal distributions of marine species, particularly marine mammals. Directed by Dr. Patrick Halpin, MGEL is a leader in applying geospatial technologies to problems in marine ecology, resource management and ocean conservation. We are seeking a highly motivated individual who is interested in modeling marine species distributions for immediate use in U.S. management actions. This is a two-year position, with extension beyond that contingent upon evaluation and funding.  The position is located at Duke University in Durham, NC, with the option to re-locate to the Duke University Marine Lab in Beaufort, NC. Salary commensurate with degree/experience.

Job title: Postdoctoral Associate or Research Scientist
Posting duration: Through October 31, 2020 or until filled
Begin date: November 1, 2020 or as soon as filled

Occupational Summary:

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 researcher 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.


The researcher will leverage knowledge and skills from a number of fields, including marine mammal ecology and biology, statistical modeling, oceanography, and scientific programming to model the density of marine mammals in the Atlantic Ocean, Mediterranean Sea, Gulf of Mexico, and/or Arctic. This will be done using distance sampling and density surface modeling methods with visual line transect surveys and environmental covariates. The primary focus will be on analyzing data collected by external collaborators, with specific tasks including: data cleaning and processing, integration of diverse datasets, fitting detections functions, creating models, and predicting density surfaces from those models. Additional responsibilities and projects may be added to the researcher's portfolio as time and mutual interests allow. These may include, for example, development of models for other study areas or taxa, development and/or application of innovative modeling approaches (e.g. modeling multiple species jointly; extrapolating models to ocean basin scale), and development of models that incorporate multiple types of data (e.g. visual surveys, passive acoustic monitoring, telemetry).

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 will collaborate with team members and external partners to accomplish the diverse tasks required to complete the analysis. This will require alternating periods of steady collaboration with intense independent analysis. This position is best suited to those who enjoy statistics, coding, manipulating and visualizing data, and other quantitative analysis tasks. 

Required Qualifications

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.


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

- 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
- Fluency in one or more European languages a plus

- Comfortable and competent working in a team on joint projects, both face-to-face and remotely
- Also able to work independently for long periods of time, with only occasional oversight
- Comfortable working on multiple projects simultaneously

Analysis and modeling
- Strong background and competency in mathematics (undergraduate calculus, at minimum).
- 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, SQL a plus.
- Demonstrated competence with GIS, geospatial analysis, and mapping. ArcMap proficiency and ability to automate production of maps and other geospatial operations preferred.
- Comfortable and competent 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.

Qualified applicants may send CV and cover letter to: https://academicjobsonline.org/ajo/jobs/16954

Duke is an Affirmative Action/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, sex, sexual orientation, or veteran status.

With best regards,

Jason Roberts, on behalf of the Marine Geospatial Ecology Lab

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