Remote Forest Carbon Jobs (NOW HIRING)

External Applicants:

Please ensure all required documents are ready to upload before beginning your application, including your resume, cover letter, and any additional materials specified in the job description.

Cover Letter and Supporting Documents:

• Navigate to the "My Experience" application page.

• Locate the "Resume/CV" document upload section at the bottom of the page.

• Use the "Select Files" button to upload your cover letter, resume, and any other required supporting documents. You can select multiple files.

Important Note: The "My Experience" page is the only opportunity to attach your cover letter, resume, and supporting documents. You will not be able to modify your application or add attachments after submission.

Current Employees:

Apply from your existing Workday account in the Jobs Hub. Do not apply from this website.

A cover letter is required for all positions; optional for facilities, campus services, and hospitality roles unless otherwise specified.

Job Description Summary

Wake Forest University invites applications for a postdoctoral research fellowship. The appointed candidate will collaborate closely with Dr. Ovidiu Csillik and collaborators to conduct cutting-edge research at the intersection of remote sensing, tropical forest carbon, and artificial intelligence (AI). The lab focuses on leveraging airborne and spaceborne lidar (such as GEDI), alongside field inventory measurements, to map and monitor forest degradation and carbon dynamics in tropical ecosystems. We particularly welcome applicants possessing a strong foundation in geospatial analysis, applied machine learning/deep learning, forest ecology, and large-scale remote sensing data processing. The successful candidates will participate in preparing presentations and scholarly articles for publication in high-tier journals. Additionally, the candidates will assist in mentoring research assistants and supporting the preparation of technical proposals.
This position is available on a one-year contractual basis, with the possibility of extension based on performance and funding availability.
Employment Terms: The position is for 1 year.

Job Description

This position is not eligible forsponsorshipof non-immigrant or immigrant visa status through Wake Forest University. All eligible applicants are encouraged to apply.

Essential Functions:

• Develops and implements advanced statistics, AI, and machine learning algorithms to process and analyze large-scale remote sensing datasets, specifically airborne lidar and spaceborne lidar (GEDI).
• Integrates field inventory measurements with remote sensing data to model tropical forest carbon stocks and monitor forest degradation.
• Plans and conduct field campaigns to collect high-resolution structural and ecological data using drones (UAVs) and terrestrial lidar systems.
• Write project reports, journal articles, conference papers, and presentations to disseminate research findings.
• Supports technical proposal preparation for developing new projects and securing grant funding.
• Contributes to the training and mentoring of undergraduate research students in the lab.

Required Education, Knowledge, Skills, Abilities:

• PhD or All but Dissertation (ABD) in Environmental Science, Forestry, Geography, Earth System Science, Computer Science, or other closely related disciplines.
• Strong publication record in the areas of remote sensing, forest ecology, carbon modeling, or applied machine learning.
• Demonstrated experience in planning and executing field data collection using drones and/or terrestrial lidar.
• Demonstrated skill in developing code for geospatial data analysis and machine learning using Python, R, and Google Earth Engine.
• Profound knowledge and hands-on experience in processing airborne lidar and spaceborne lidar (GEDI) datasets.
• Experience with field inventory data and statistical approaches for scaling plot-level measurements to regional or global scales.
• Outstanding skills in interpersonal communication, scientific writing, and effective time management.
• Capability to work independently and collaboratively in a cooperative team setting.

Preferred Education, Knowledge, Skills, Abilities:

• Specific research experience working with tropical forest ecosystems and mapping forest degradation.
• Proficiency with advanced computer vision and deep learning techniques applied to satellite imagery and 3D point cloud data.
• Experience with high-performance computing (HPC) or cloud computing environments for handling massive geospatial datasets.
• FAA Part 107 Remote Pilot Certificate for commercial drone operations.

Accountabilities:

• Responsible for own work.

Physical Requirements:

• Moderate physical activity: Mainly working on computer algorithms and large-scale data analysis. Conducting fieldwork may require hiking, carry equipment (such as drones), and navigate forested environments.

Environmental Conditions:

• No environmental conditions.

Additional Job Description

Time Type Requirement

Full timeNote to Applicant:

This position profile identifies the key responsibilities and expectations for performance. It cannot encompass all specific job tasks that an employee may be required to perform. Employees are required to follow any other job-related instructions and perform job-related duties as may be reasonably assigned by his/her supervisor.

In order to provide a safe and productive learning and living community, Wake Forest University conducts background investigations and drug screens for all final staff candidates being considered for employment.

Equal Opportunity Statement

The University is an equal opportunity employer and welcomes all qualified candidates to apply without regard to race, color, religion, national origin, sex, age, sexual orientation, gender identity and expression, genetic information, disability and military or veteran status.

Accommodations for Applicants

If you are an individual with a disability and need an accommodation to participate in the application or interview process, please contact AskHR@wfu.eduor (336) 758-4700.