Job Description Summary

POSITION SUMMARY:
The Department of Geographical Sciences at the University of Maryland, College Park, in partnership with NASA Harvest, is seeking a Postdoctoral Associate to contribute to research at The intersection of satellite remote sensing, crop modeling, machine learning, and food security.

The position is housed within the yield modeling group at NASA Harvest and will support projects focused on agricultural monitoring, crop yield forecasting, and the development of scalable, AI-enabled geospatial data systems for Sub-Saharan Africa and other food-insecure regions.

POSITION OVERVIEW:
The successful candidate will work under the supervision of Dr. Ritvik Sahajpal and collaborate with an international network of partners, including FAO, Microsoft AI for Good, GEOGLAM, and other NASA Harvest Consortium members. The role offers an opportunity to contribute to high-impact, operational systems that directly support national governments, international organizations, and development partners in making evidence-based agricultural and food security decisions.

• Develop and improve machine learning and deep learning models for crop yield
forecasting and agricultural monitoring at subnational to national scales.
• Integrate process-based crop simulation models (e.g., EPIC, DSSAT, APSIM) with data-driven approaches, including knowledge-guided machine learning (KGML)
frameworks.
• Process, analyze, and extract features from multi-source satellite remote sensing data (optical, SAR, thermal) using cloud computing platforms such as Google Earth Engine.
• Contribute to the design and implementation of scalable, operational geospatial data pipelines for agricultural information systems.
• Collaborate with international partners to validate models against ground truth and official agricultural statistics across multiple countries.
• Publish findings in peer-reviewed journals and present results at scientific conferences and to stakeholders in government and international organizations.
• Mentor and support graduate and undergraduate research assistants as needed.
• Support and deliver training at workshops for partners, stakeholders, and
capacity-building events.

MINIMUM QUALIFICATIONS:
• Ph.D. in remote sensing, geospatial science, agricultural engineering, computer science, environmental science, or a closely related field.
• Strong programming skills in Python, with demonstrated experience building scientific computing workflows and data pipelines.
• Demonstrated expertise in machine learning or deep learning applied to geospatial or agricultural problems (e.g., crop classification, yield prediction, anomaly detection).

PREFERRED QUALIFICATIONS:
• Experience with or strong interest in process-based crop simulation models and their integration with ML/AI approaches.
• Ability to work with large geospatial datasets and cloud computing platforms (Google Earth Engine, or similar).
• Strong written and oral communication skills, including experience with scientific publications.
• Ability to work independently and collaboratively within a multidisciplinary, international research team.
• Experience with satellite remote sensing data processing and analysis (e.g. Sentinel-2, Landsat, MODIS/VIIRS, SAR).

LICENSES/ CERTIFICATIONS:
N/A

PHYSICAL DEMANDS:
N/A

Additional Job Details

Financial Disclosure Required

No

Department

BSOS-Geography

Worker Sub-Type

Faculty Regular

Salary Range

$85,000 - $95,000

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

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