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The Machine Learning and Sensing Laboratory at the University of Florida has an opening for a post-doctoral research associate to develop computer vision and machine learning algorithms for a novel back-scatter x-ray system. The position will be a part of the University of Florida’s “Rays for Roots” project funded by ARPA-E ROOTS program. The researcher will develop novel super-resolution, image processing and root-vs-soil segmentation algorithms, automated feature extraction algorithms, and machine learning methods to map image features to measured values such as soil moisture, soil porosity, and other parameters of interest. The overall project, Rays for Roots – Integrating Backscatter X-Ray Phenotyping, Modeling, and Genetics to Increase Carbon Sequestration and Resource Use Efficiency, aims to develop a high-throughput system for plant root phenotyping using back-scatter x-ray technology in the field. The project team is highly interdisciplinary consisting of computer scientists, plant and soil scientists, nuclear engineers, electrical engineers, and plant modelers providing a unique post-doctoral research experience. Candidates must have a Ph.D. in computer science, computer engineering or a related area with expertise in image processing, image/data analysis and machine learning. To apply: Please send your CV, publication samples, and list of references to Dr. Alina Zare (firstname.lastname@example.org); Lab website: https://faculty.eng.ufl.edu/alina-zare/; ROOTS project descriptions: http://bit.ly/2jtVxGL.