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<div class="">PhD student position in Machine Learning: Theoretically motivated deep learning</div>
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<div class="">At the Division of Computational Science and Technology at KTH we are seeking a new PhD student in Machine Learning / Computer Vision to handle scale-dependent information in image data.</div>
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<div class="">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 as well as experimental evaluations 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.</div>
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<div class="">For examples of our previous work in this area, see </div>
<div class=""><a href="https://www.kth.se/profile/tony/page/deep-networks" class="">https://www.kth.se/profile/tony/page/deep-networks</a></div>
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<div class="">Within the scope of this PhD student position, you will work on and contribute to the research frontier regarding scale-covariant or scale-equivariant deep networks and/or deep networks parameterised in terms of Gaussian derivatives, on specific
research topics that we choose together within the scope of the research project ”Covariant and invariant deep networks” that finances this position. The overall goal is to develop new architectures for deep networks that can generalise to scaling variations
that are not spanned by the training data, and which can achieve higher robustness to variabilities in test data, as well as enable more efficient training with lower requirements concerning the amount of training data.</div>
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<div class="">The candidate should have very good knowledge in mathematics (analysis and linear systems, which we use for modelling convolution transformations and geometric image transformations) as well as in structured programming to write code that is easy
to use for making experiments with, maintain and develop and share with colleagues. You must have very good knowledge about programming deep networks in Python, PyTorch is meritorious.</div>
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<div class="">Knowledge in computer vision and image analysis is strongly meritorious.</div>
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<div class="">For further information and information about how to apply, see</div>
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<div class=""><a href="https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:567595/where:4/" class="">https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:567595/where:4/</a></div>
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<div class=""><span><img apple-inline="yes" id="8AFD7D91-7967-42DE-8B88-15E03E0E6F05" src="cid:FD2B374F-C472-4895-ADA7-B3CD5419FD22@csc.kth.se" class=""></span>
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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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