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<b><span style="font-family:"Times New Roman",serif;color:black">Deep Learning and Image Processing for Transformational Environmental Science</span></b><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<span style="font-family:"Times New Roman",serif;color:black">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. </span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<span style="font-family:"Times New Roman",serif;color:black">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.</span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<span style="font-family:"Times New Roman",serif;color:black">In particular applicants are sought for:</span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<b><span style="color:black">Texture-based Analysis of Environmental Image Data.</span></b><span style="color:black"> 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.</span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<b><span style="color:black">Requirements:</span></b><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<span style="color:black">Ph.D. focused on computer graphics, vision or related area.<span class="apple-converted-space"> </span></span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<span style="color:black">Please send CV and letter expressing your particular areas of interest to Holly Rushmeier,<span class="apple-converted-space"> </span><a href="mailto:holly@acm.org"><span style="color:#0563C1">holly@acm.org</span></a>, by DECEMBER
10.</span><span style="font-size:10.0pt;color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">----------<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;color:black">Holly Rushmeier<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;color:black">John C. Malone Professor of Computer Science<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Tahoma",sans-serif;color:black">Yale University, PO Box 208285, New Haven, CT 06520-8285</span><span style="font-size:11.0pt;color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Tahoma",sans-serif;color:black">Phone: <a href="tel:(203)432-4091" title="tel:(203)432-4091"><span style="color:#0563C1">(203)432-4091</span></a>, <a href="mailto:holly@acm.org"><span style="color:#0563C1">holly@acm.org</span></a></span><span style="font-size:11.0pt;color:black"><o:p></o:p></span></p>
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