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<b class="">Postdoc in Computer Vision / Machine Learning / Applied Mathematics</b></p>
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<b class="">Job description </b></p>
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The Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm, Sweden is seeking a Postdoc in Computer Vision / Machine Learning / Applied Mathematics to handle scale-dependent image information in deep networks. </p>
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In our research, we develop deep networks for processing image data that handle scaling transformations and other image transformations in a theoretically well-founded manner. Our research in this area comprises both theoretical modelling of the influence of
image transformations on different architectures for deep networks and the experimental evaluation of such networks on benchmark datasets to explore their properties. The work also comprises the creation of new benchmark datasets, to enable characterization
of properties of deep networks that are not covered by existing datasets. For examples of our previous work in this area, see
<a href="https://www.kth.se/profile/tony/page/deep-networks" class="">https://www.kth.se/profile/tony/page/deep-networks</a> </p>
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Within the scope of this postdoc position, you are expected to work on and contribute to the research frontier regarding scale-covariant or scale-equivariant deep networks and/or deep networks parameterized in terms of Gaussian derivatives, on specific research
topics that we choose together. </p>
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The selected candidate will work closely together with the project leader Tony Lindeberg. </p>
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<b class="">Qualifications requirements </b></p>
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<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline </span>
</li><li style="margin: 0px 0px 12px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";" class="">
<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">We are seeking a candidate with a PhD in Computer Vision, Machine Learning, Applied Mathematics or a related discipline
dealing with automated analysis of image information. </span> </li><li style="margin: 0px 0px 12px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";" class="">
<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">Previous experience with experimental evaluations using deep learning architectures applied to image data is necessary,
preferably PyTorch.</span> </li><li style="margin: 0px 0px 12px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";" class="">
<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">A theoretical background in continuous mathematics for modelling convolutions and the influence of image transformations
on image data is also necessary. </span> </li></ul>
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<b class="">Preferred qualifications </b></p>
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As a person you have excellent scientific and collaborative skills, in combination with independence, with very good ability to get into new scientific theories and conduct implementations and experimental evaluations in close collaboration with the research
environment you are working in. </p>
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The preferred candidates should have demonstrated expertise (through publications) in any one of the following: </p>
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<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">Deep networks that handle image information for computer vision tasks, including experimental evaluation using modern
architectures for deep networks.</span> </li><li style="margin: 0px 0px 12px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";" class="">
<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">Continuous models for deep networks applied to image information.</span>
</li><li style="margin: 0px 0px 12px; font-stretch: normal; line-height: normal; font-family: "Helvetica Neue";" class="">
<span style="font-stretch: normal; font-size: 14.4px; line-height: normal;" class=""></span><span style="background-color: #ffffff" class="">Theoretical modelling of scaling transformations or other image transformations applied to automated processing of image
information. </span> </li></ul>
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For further information and information about to how to apply, see <a href="https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:490734/" class="">
https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:490734/</a> </p>
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Application deadline: May 2, 2022</p>
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The position is offered for a period of two years.</p>
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Tony Lindeberg</div>
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Professor of Computer Science <span style="background-color: rgb(255, 255, 255); color: rgb(119, 119, 119); font-family: Arial, sans-serif; font-size: 13px; line-height: 16px;" class="">— </span>Computational Vision</div>
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KTH Royal Institute of Technology</div>
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Computational Brain Science Lab</div>
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Division of Computational Science and Technology, CST</div>
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SE-100 44 Stockholm, Sweden</div>
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Phone: +46 8 790 6205</div>
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<a href="https://www.kth.se/profile/tony/" class="">https://www.kth.se/profile/tony/</a></div>
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