FA27-026: Postdoctoral Research Faculty Fellow

SIUE; Edwardsville (IL); On-site; USD 72,000 - 78,000; Full time

Job summary

The Geospatial Mapping, Applications, and Research Center (GeoMARC) at Southern Illinois University Edwardsville invites applications for a two-year Research Faculty Fellow (Postdoctoral Scholar) in Remote Sensing and Environmental Analytics. The fellow will apply ML and geospatial technologies to support externally funded research and environmental decision-making.

Working with GeoMARC’s experienced team, the fellow will develop innovative workflows, contribute to publications, and help build a

Qualifications

• Ph.D. required by appointment date.
• Demonstrated expertise in remote sensing, image processing, and machine learning for environmental monitoring.
• Experience with satellite and UAS remote sensing data, including RGB, multispectral, and LiDAR datasets.
• Proficiency with ArcGIS Pro, Google Earth Engine, and ERDAS IMAGINE or comparable software.
• Strong quantitative and statistical skills and ability to manage multiple projects.

Responsibilities

• Contribute expertise in remote sensing, image analysis, and ML to a diverse research portfolio.
• Develop innovative analytical workflows and publish results in peer-reviewed venues.
• Collaborate with GeoMARC faculty to translate data into actionable environmental insights.

Skills

Remote sensing

Machine learning

Python programming

Geospatial analysis

Image processing

Education

Ph.D. in Geography, Environmental Science, Remote Sensing, Geospatial Science, or closely related field

Tools

ArcGIS Pro

Google Earth Engine

ERDAS IMAGINE

Job description

Southern Illinois University Edwardsville: Graduate Students and Research: GeoMARC

Location

Edwardsville, IL

Open Date

Sep 09, 2026

Salary Range or Pay Grade

$72,000 - $78,000

Deadline

Oct 08, 2026 at 11:59 PM Eastern Time

Description

The Geospatial Mapping, Applications, and Research Center (GeoMARC) at Southern Illinois University Edwardsville invites applications for a two-year Research Faculty Fellow (Postdoctoral Scholar) appointment in Remote Sensing and Environmental Analytics. GeoMARC has a long history of conducting applied research for federal and state agencies, utility providers, agricultural organizations, and private industry partners, with projects focused on environmental monitoring, environmental quality assessment, natural resource management, and geospatial decision support. Current research utilizes satellite, airborne, uncrewed aerial systems (UAS), and LiDAR data to address challenges related to agriculture, energy infrastructure, biodiversity, land-use change, ecosystem monitoring, and environmental sustainability.

The successful candidate will contribute expertise in remote sensing, image analysis, machine learning, and AI-driven geospatial technologies to support and advance a diverse portfolio of externally funded research and applied geospatial projects. Working closely with GeoMARC ‘s highly experienced research team, the Research Faculty Fellow will contribute to the development of innovative analytical approaches and workflows that help transform remotely sensed data into actionable information for environmental decision-making and resource management.

The Research Faculty Fellow will also be provided with the opportunity to work closely with GeoMARC’s faculty to synthesize findings from past and ongoing projects into peer-reviewed publications, technical reports, and conference presentations. Through exposure to (and participation in) a broad range of active research initiatives, the Fellow will gain extensive experience applying remote sensing, image analysis, machine learning, and geospatial technologies to real-world environmental challenges while building a strong publication record and developing a comprehensive understanding of GeoMARC's research mission, methodologies, and partnerships.

Building on this experience, the Fellow will contribute to the continued growth of GeoMARC by helping identify emerging research opportunities and funding initiatives related to the use of remote sensing and geospatial analysis for environmental monitoring, environmental quality, agriculture, and energy-sector applications. Working alongside GeoMARC’s leadership, the Fellow will assist in the development of competitive grant proposals and collaborative research partnerships while gaining valuable experience in sponsored research development and the operation of a multidisciplinary applied research center.

Qualifications

• Ph.D. in Geography, Environmental Science, Remote Sensing, Geospatial Science, or a closely related field by the time of appointment.
• Demonstrated expertise in remote sensing, digital image processing, and machine learning, particularly as applied to environmental monitoring, environmental quality assessment, land-use and land-cover change analysis, and landscape-scale feature detection.
• Demonstrated experience working with satellite remote sensing data (e.g., Planet Labs, Sentinel, Landsat) and UAS-based remote sensing data, including RGB, multispectral, and LiDAR datasets.
• Proficiency with ArcGIS Pro, Google Earth Engine, and ERDAS IMAGINE or comparable image processing and geospatial analysis software.
• Strong proficiency in Python programming, with experience developing automated geospatial workflows and processing large geospatial datasets.
• Strong quantitative and statistical skills, including experience with sampling design, model development, validation procedures, accuracy assessment, and uncertainty analysis.
• Demonstrated ability to manage multiple projects and deadlines in a collaborative, applied research environment.
• Excellent written and verbal communication skills, including the ability to prepare technical reports, peer-reviewed publications, grant proposals, and professional presentations.

Preferred Qualifications

• Knowledge of vegetation ecology, plant community characteristics, and environmental monitoring applications.
• Experience developing and implementing machine learning and deep learning approaches for classification, regression, object detection, or predictive modeling.
• Experience evaluating model performance through pre-modeling data assessment, feature selection, validation, and post-modeling accuracy and uncertainty analyses.
• Demonstrated experience preparing research proposals, managing externally funded projects, or contributing to interdisciplinary research teams.
• FAA Part 107 Remote Pilot Certification or the ability to obtain certification within six months of appointment.

Application Instructions

Applicants will be subject to a background check prior to an offer of employment.

Employment is contingent upon satisfying all legal requirements as set forth by the U.S. Citizenship and Immigration Services in regard to employability in the United States. SIUE makes no representation, warranty, or agreement that third parties, including government agencies, will approve submitted documents, applications, or petitions for an H-1B visa or Optional Practical Training.

SIUE is a state university - benefits under state sponsored plans may not be available to holders of F1 or J1 visas. The State of Illinois requires that immigrant employees achieve H-1 status before they become eligible for certain benefits.

In compliance with the ADA Amendments Acts (ADAAA), if you have a disability and would like to request an accommodation in order to apply for a position with Southern Illinois University Edwardsville, please call 618-650-2190 or email [e-mail].

The SIUE Annual Security and Fire Safety Report is available online at https://www.siue.edu/police/cleary-act-forms/ . This report is published in compliance the “Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act” and the Higher Education Opportunity Act also known as the “Campus Fire Safety Right to Know.”

Application Process

Southern Illinois University Edwardsville is an equal opportunity employer and thus prohibits discrimination against employees, applicants for employment and students on the basis of age, color, disability, marital status, national origin, race, religion, sex, sexual orientation, or veteran's status.