[ES_JOBS_NET] Post-doc in New Haven CT, Computer Vision/Image Analysis, apply by Dec 10

[Rushmeier, Holly]

Deep Learning and Image Processing for Transformational Environmental Science

This project focuses on assembling a cohort of two postdocs with backgrounds in Image Processing and Machine Learning who will pursue projects in collaboration with a range of Ecological Science researchers. This cohort would additionally help to solidify a foundation for a range of initiatives at the intersection of Applied Computing and Environmental/Ecological Sciences at Yale.

High-resolution imaging sensing has enabled the production of huge amounts of data that is informationally-dense and relatively easy to share. In Ecological and Environmental Sciences, a number of promising opportunities exist to leverage these. Using traditional image processing techniques such as object detection and segmentation, for example, a wide range of modelling activities can be performed in order to quantify various aspects of plants and animals such as for phenotyping or other classification activities. Furthermore, with the increased proliferation of high quality and inexpensive sensors comes a proportional increase in the amount of data from them that must be analyzed. There are a number of directions at the intersection of machine learning and signal processing that are very promising, allowing functions such as automatically “filtering” datasets (such as to remove uninteresting clips), processing for automatic ID of animals, and many more.



In particular applicants are sought for:



Texture-based Analysis of Environmental Image Data. Textural information in images can provide a rich suite of information about the content that isn’t immediately apparent from simpler image characteristics such as color or saturation. This postdoc would work on two projects related to texture: 1) Correlating animal health metrics -  texture in images, such as from a trap camera campaign, can provide information about the health of the individual animals documented. 2) Remote sensing landscapes - Differentiating plantation vs natural forest regeneration in tropical forest landscapes from satellite images, for instance, is not yet possible through traditional approaches. Texture-based approaches can likely resolve remote sensing images to a much greater extent than currently possible.



Requirements:

Ph.D. focused on computer graphics, vision or related area.



Please send CV and letter expressing your particular areas of interest to Holly Rushmeier, holly at acm.org<mailto:holly at acm.org>, by DECEMBER 10.




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Holly Rushmeier
John C. Malone Professor of Computer Science
Yale University, PO Box 208285, New Haven, CT 06520-8285
Phone: (203)432-4091<tel:(203)432-4091>, holly at acm.org<mailto:holly at acm.org>



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