Research Assistant

The University of Georgia; Athens (GA); On-site; USD 22,000 - 34,000; Full time

Job summary

The University of Georgia invites a graduate student (MS or PhD) in Computer Science, Engineering, AI, or Geospatial Science to join a research team led by Dr. Tao Liu.

The project focuses on AI applications with remote sensing data, developing geospatial foundation models for land cover classification and monitoring key forest metrics. You will conduct literature reviews, design and run experiments, and draft manuscripts for publication in peer‑reviewed journals, collaborating closely with the

Qualifications

• Graduate student in a relevant field (MS or PhD).
• Experience developing deep learning models using PyTorch.
• Experience applying ML/DL to remote sensing imagery.

Responsibilities

• Conduct literature reviews and identify research gaps related to the topic.
• Design and implement the proposed model and run experiments.
• Prepare and write manuscripts for peer‑reviewed journals.

Skills

Python

Deep learning

Remote sensing

Education

Master’s or Ph.D. in Computer Science, Engineering, AI, Geospatial Science

Tools

PyTorch

Job description

Position Summary

The student will work under the supervision of Dr. Tao Liu to conduct research and contribute to scholarly publications on AI applications using remote sensing data. The primary focus will be on developing and applying geospatial/remote sensing foundation models for land cover classification and monitoring key forest metrics.

Duties/Responsibilities

• Conduct comprehensive literature reviews and collaborate with the Principal Investigator (PI) to identify research gaps related to the proposed research topic. (30% of time)
• Design and implement the proposed model, and conduct experiments to establish proof of concept. (40% of time)
• Prepare and write the manuscript for submission to a peer-reviewed journal. (30% of time)

Relevant/Preferred Education, Experience, Licensure, Certification in Position

• Graduate student (Master’s or Ph.D.) in Computer Science, Engineering, Artificial Intelligence, Geospatial Science, or a related field
• Experience developing deep learning models using PyTorch
• Experience applying machine learning or deep learning methods to remote sensing imagery

Knowledge, Skills, Abilities and/or Competencies

• Proficiency in deep learning frameworks (preferably PyTorch)
• Familiarity with remote sensing data processing and analysis
• Basic knowledge of GIS concepts and workflows
• Experience working with geospatial data formats, including:
  • Vector data (e.g., shapefiles, .shp)
  • Raster data (e.g., GeoTIFF)
• Strong programming skills (e.g., Python)
• Ability to work independently and collaboratively in a research environment

Physical Demands

The student will attend weekly meetings in person and report the project progress.

Background Investigation Policy

Offers of employment are contingent upon completion of a background investigation including, a criminal background check demonstrating your eligibility for employment with the University of Georgia; confirmation of the credentials and employment history reflected in your application materials (including reference checks) as they relate to the job-based requirements of the position applied for; and, if applicable, a satisfactory credit check. You may also be subject to a pre-employment drug test for positions with high-risk responsibilities, if applicable. Please visit the UGA Background Check website.

Contact Details

For questions concerning this position or recruitment progression, please refer to the Recruitment Contact listed below.

Recruitment Contact Name: Tao Liu

Recruitment Contact Email: [e-mail]

EOO Statement

The University of Georgia is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, disability, genetic information, national origin, race, religion, sex, or veteran status or other protected status. Persons needing accommodations or assistance with the accessibility of materials related to this search are encouraged to contact Central HR ([e-mail]).