"Post-Doctoral Fellowship"

We are seeking a post-doctoral fellow in the field of Deep Learning for Remote Sensing applications, focusing on quantitative parameters or land cover classification. The primary responsibility of the candidate will be to conduct research by collecting and processing medium- to high-resolution satellite data (such as Landsat, Sentinel-2, and PlanetScope) and adapting and applying deep learning models for information extraction. The candidate will also prepare manuscripts and presentations describing their research. The salary and benefits are nationally competitive.

The position will be located in the Geospatial Sciences Center of Excellence (GSCE). The GSCE is a joint venture linking South Dakota State University (SDSU) with the United States Geological Survey's Center for Earth Resources Observation and Science (EROS). SDSU is ranked highly in the subject area of Remote Sensing. The GSCE is a friendly research environment that has excellent research infrastructure and computing support.

The candidate must have a PhD degree in remote sensing or a related scientific field and experience in deep learning applications. Preferred candidates will have expertise in at least two or several of the following areas:

• Proficiency in Python (additional languages a plus); Linux/Unix experience preferred
• Experience processing optical (e.g., Landsat, Sentinel-2) and SAR (e.g., Sentinel-1) remote sensing data; time-series analysis a plus
• Experience with foundation models, including pretraining and fine-tuning
• Strong record of peer-reviewed publications

To apply: Please send a curriculum vitae, a brief statement of research experiences and goals (≤1 page), and contact information for three references to Dr. Hankui Zhang, Geospatial Sciences Center of Excellence, South Dakota State University, Wecota Hall, Box 506B, Brookings, SD 57007-3510, USA (via email to: hankui.zhang@sdstate.edu). South Dakota State University is committed to affirmative action, equal opportunity and the diversity of its faculty, staff and students. Review of applications will begin on Oct 1st 2025 and continue until the position has been filled.