The aim of this propose is to select a postdoc that will engage in a thematic project financed by FAPESP, entitled High Resolution Remote Sensing For Digital Agriculture. The postdoc selected will be involved in developing a new approach for accurate estimation of orange fruit production by using Terrestrial Laser Scanning (TLS) data and machine/deep learning (ML/DL) methods. This project will be developed at Unesp – São Paulo State University, campus of Presidente Prudente-SP, Brazil.
The desirable characteristics for the candidates are as follows: PhD degree in Remote Sensing, Photogrammetry, or related sciences; solid background in Photogrammetry, Remote Sensing, Adjustment of Observations, Cartography and related subjects; strong knowledge of programming involving C, C++, Python, among other languages; good knowledge of DL/DP theory, algorithm, and implementation; experience with TLS LiDAR data processing; good communication skills in English and willingness to work in an interdisciplinary team; good record of peer-reviewed publications in the related field; availability for frequent field works; and full-time dedication to the project.
The duration of this fellowship is 24 months, with the possibility of extension, upon FAPESP evaluation.

https://drive.google.com/file/d/1uQbf3QAcpIm76bJeuz3qsmP6Uuymxqr-/view?usp=sharing.

PhD./Postdoc – The Ohio State University, Photogrammetry, Computer Vision

If you are enthusiastic to work at the conjunction of Computer Vision and Geomatics, a few funded Ph.D./Postdoc Positions are available in our group at The Ohio State University. We are solving view rendering, generation, registration challenges with geospatial fidelity at all levels. Research are in collaboration with computer vision groups at multiple institutes, such as Stanford Research Institute (SRI), Massachusetts Institute of Technology (MIT), University of Southern California (USC), University of California Irvine (UCI), on monthly basis. 

Degree registration follows a general principle list under our page here (https://u.osu.edu/qin.324/opportunities/). Positions can start immediately. If you are interested, please email Dr. Qin (qin.324@osu.edu) your CV that best describes yourself in alignment with qualification specified as follows. We embrace teammates who are critical thinker, quick learner, actionist, committed team player, please do not be shy to mention that in your email if you consider yourself highly in these aspects.

Education: Computer science, Geomatics, Strong in Programming and Math

Field: Computer Vision, Photogrammetry, Vision-based Machine Learning

Preferred skillsets (prioritized in order):

1. Proficient in Structure from Motion/Photogrammetry/Multi-view image matching, error modeling.
2. Experiences in Neural Radiance Field and GAN, Diffusion Models.
3. Experience in Intrinsic Image Decomposition, Inverse Rendering
4. Experience in Satellite Photogrammetry and sensor modeling
5. Student with a master’s degree preferred, ok with a bachelor’s degree if you consider yourself competitive.

Preferred+:

1. Master in C++, ability to work across different coding language to build workflow
2. Have one or more good publications that you are a major contributor to
3. Good communication/writing skills.