Duke University, Nicholas School of the Environment - Durham

Position ID:

Duke-NSOE-Durham-POSTDOC_QIU [#29214]

Position Title:

Postdoctoral Associate - Ecological Modeling

Position Type:

Postdoctoral

Position Location:

Durham, North Carolina 27708, United States of America

Subject Areas:

Computer Science
Modeling
Ecology
Climate Science
Environmental Science (more...)

Appl Deadline:

(posted 2024/11/11, listed until 2025/05/11)

Position Description:

The Qiu lab at the Nicholas School of the Environment at Duke University invites applications for 1-2 highly motivated Postdoctoral Associates focused on forest dynamics and biodiversity under climate and habitat change. We seek candidates with experience in integrating multi-source remote sensing with big ecological data from landscape to global scales. The ideal candidates will utilize various remote sensors (e.g., LiDAR, hyperspectral, Terrestrial Laser Scanning, Drones, etc.) alongside diverse ecological data (e.g., forest inventory, monitoring network, camera traps imagery, GBIF data, etc.). Responsibilities include developing and applying advanced statistical models, machine learning, and deep learning approaches to assess multiple dimensions of forest biodiversity across scales and trophic levels. As such, we seek applicants with strong quantitative backgrounds in remote sensing (particularly LiDAR and hyperspectral data) and ecological modeling. The selected postdocs will contribute to data analysis and visualization, fieldwork, mentoring undergraduate and graduate students, manuscript preparation, and presenting research at conference. They will also have the opportunity to leverage resources such as the Duke Forest, high-performance computing clusters, Riegl VZ-400i, and the Drone Lab, while collaborating with researchers at UNC and NC State University.

TERM 1 year, with the possibility of extension based on performance and funding.

QUALIFICATIONS Applicants must hold a Ph.D. in Ecology, Environmental Science, Climate Science, Computer Science, Geography, or a related field. We are looking for candidates with a strong interest in ecology and quantitative modeling, along with training in programming languages such as R, Python, and/or MATLAB. Candidates should have advanced skills in programming (e.g., R or Python) for processing and visualizing geospatial data, particularly LiDAR and hyperspectral data, as well as ecological datasets. Experience with high-performance computing environments is highly preferred. A strong record of peer-reviewed publications is also desirable. Proficiency in spoken and written English is required.

PROJECTED START DATE The position is available immediately. Target start date for the position is flexible, but ideally should start in the spring 2025.

Application Deadline: Review of applications will begin immediately and continue until the position is filled. Questions should be emailed to tong.qiu@duke.edu

Duke University 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.

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.

Application Materials Required:

Submit the following items online at this website to complete your application:

Curriculum Vitae
Two sample research papers
One-page cover letter describing your background and career goals
Two-page statement of research interests and methodological skills
Three references (no actual letters, just names and email addresses )

And anything else requested in the position description.

Further Info: