Job description

Job Description Summary Organizational Summary Statement: The Assistant Research Scientist will work as a Professional Track Research Faculty member in the Department of Environmental Science & Technology (ENST) at the University of Maryland, College Park (UMD). The primary objective of this person will be to characterize and assess ecosystem service provisions associated with natural and restored wetlands within the Mid- Atlantic region. This will include utilization of multi-sensor remote sensing data and application of machine learning and deep learning methods, with the support of high-performance computers. We are looking for candidates who have research experience in wetland/agricultural ecosystems, exceptionally strong skills in GIS & remote sensing applications, and relevant experiences in machine learning/deep learning modeling. This individual will assist in leading collaborative research efforts with the USDA-ARS and other federal partners. Responsibilities: ● Participate in development of collaborative research projects on mapping agricultural landscapes and assessing wetland status and ecosystem service provisions. ● Determine how to best facilitate the use of advanced machine learning/deep learning techniques in quantification of wetlands in agricultural landscapes. ● Integrate multi-sensor remote sensing datasets (e.g., lidar, optical, and radar) to initiate models designed to explore multi-scale wetland ecosystem dynamics and ecosystem service provision in agricultural landscapes as influenced by human activities. ● Develop and manage machine learning/deep learning models utilizing various remote sensing datasets with the support of high-performance computers. ● Develop deep learning workflows for generating topographic and landscape models that characterize complex agricultural watersheds using remote sensing datasets. ● Prepare presentations, reports, and publications of journal articles and communicate deliverables to sponsors. Minimum Qualifications: ● Earned doctorate (Ph.D.) in ecology, biology, geography, or environmental science. ● Demonstrated expertise in combining GIS and Remote Sensing data with agroecosystem models to assess effects of agricultural conservation practices. ● Experience in classification of natural and agricultural ecosystems using machine learning and deep learning tools (minimum five years). ● Demonstrated experience with collection of field samples (soils, plants, etc.) for testing and evaluating model-based predictions. ● Experience in classification of wetland ecosystems using machine learning tools (minimum five years). ● Demonstrated ability to publish in scientific journals in the areas of Remote Sensing, GIS, Machine learning and Deep learning as applied to natural and agricultural systems. Physical Demands: Ability to occasionally perform indoor and outdoor work related to collection of soil and plant samples and related analyses, requires the lifting and carrying (up to 30 lbs.), standing, kneeling, bending, stooping, and reaching. Licenses/ Certifications: N/A Additional Job Details Preferred Qualifications: ● Experience in using High-Performance computing tools (three years preferred). ● Expertise in GIS/remote sensing support techniques and programming skills including python/R/MATLAB languages (three years preferred). ● Experience managing models utilizing various remote sensing datasets with the support of high-performance computers, and especially deep learning tools (three years preferred). ● Demonstrated familiarity with USDA conservation programs and with specific conservation practices falling within the purview of CEAP. Required Application Materials: (1) a cover letter, (2) a current CV or resume, and (3) contact information for three references Best Consideration Date: 10/29/2026 Posting Close Date: 10/29/2026 Open Until Filled: N/A Candidate must be eligible to work in the United States at the time of hiring. The department will not provide sponsorship for work-visa requirements. Financial Disclosure Required No For more information on Financial Disclosure, please visit Maryland s State Ethics Commission website . Department AGNR-Environmental Science & Technology Worker Sub-Type Faculty Regular Salary Range $80,000 to $90,000 Benefits Summary For more information on Regular Faculty benefits, select this link . Background Checks Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify anyone from employment. Before any adverse decision, the finalist will have an opportunity to provide information to the University regarding disclosable background check information. The University reserves the right to rescind the offer of employment or otherwise decline or terminate employment if the information reported by the background check is deemed incompatible with the position, regardless of when the background check is completed. Employment Eligibility The successful candidate must complete employment eligibility verification (on Form I-9) by presenting documents that establish identity and work authorization within the timeframe required by federal immigration law, and where applicable, to demonstrate renewed employment authorization. Failure to complete employment eligibility verification or reverification within the timeframe set forth by law may result in suspension or termination of employment. EEO Statement The University of Maryland, College Park is an Equal Opportunity Employer. All qualified applicants will receive equal consideration for employment. Please read the University’s Equal Employment Opportunity Statement of Policy. Title IX Non-Discrimination Notice Resources Learn how military skills translate to civilian opportunities with O*Net Online Search Firm Managed Recruitment There are some positions that are not advertised on this career site as the search is being managed by a Search Firm. Please visit the link below to see these available opportunities: Search Firm Managed Vacancies

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Must-haves

Job Description Summary Organizational Summary Statement:
The Assistant Research Scientist will work as a Professional Track Research Faculty member in
the Department of Environmental Science & Technology (ENST) at the University of Maryland,
College Park (UMD). The primary objective of this person will be to characterize and assess
ecosystem service provisions associated with natural and restored wetlands within the Mid-
Atlantic region. This will include utilization of multi-sensor remote sensing data and application
of machine learning and deep learning methods, with the support of high-performance
computers. We are looking for candidates who have research experience in wetland/agricultural
ecosystems, exceptionally strong skills in GIS & remote sensing applications, and relevant
experiences in machine learning/deep learning modeling. This individual will assist in leading
collaborative research efforts with the USDA-ARS and other federal partners.
Participate in development of collaborative research projects on mapping agricultural
landscapes and assessing wetland status and ecosystem service provisions.
Determine how to best facilitate the use of advanced machine learning/deep learning
techniques in quantification of wetlands in agricultural landscapes.
Integrate multi-sensor remote sensing datasets (e.g., lidar, optical, and radar) to initiate
models designed to explore multi-scale wetland ecosystem dynamics and ecosystem
service provision in agricultural landscapes as influenced by human activities.
Develop and manage machine learning/deep learning models utilizing various remote
sensing datasets with the support of high-performance computers.
Develop deep learning workflows for generating topographic and landscape models that

Nice-to-haves

Experience in using High-Performance computing tools (three years preferred).
Expertise in GIS/remote sensing support techniques and programming skills including python/R/MATLAB languages (three years preferred).
Experience managing models utilizing various remote sensing datasets with the support of high-performance computers, and especially deep learning tools (three years preferred).

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machine learningpython

Platform: workday
Effort: long

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